Cognitive learning system having a cognitive graph and a cognitive platform

ABSTRACT

A cognitive information processing system environment comprising a plurality of data sources; a cognitive inference and learning system coupled to receive data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to perform a cognitive learning operation, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; and, a destination, the destination being updated based upon the learning result.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for performing cognitive inference and learningoperations.

Description of the Related Art

In general, “big data” refers to a collection of datasets so large andcomplex that they become difficult to process using typical databasemanagement tools and traditional data processing approaches. Thesedatasets can originate from a wide variety of sources, includingcomputer systems, mobile devices, credit card transactions, televisionbroadcasts, and medical equipment, as well as infrastructures associatedwith cities, sensor-equipped buildings and factories, and transportationsystems. Challenges commonly associated with big data, which may be acombination of structured, unstructured, and semi-structured data,include its capture, curation, storage, search, sharing, analysis andvisualization. In combination, these challenges make it difficult toefficiently process large quantities of data within tolerable timeintervals.

Nonetheless, big data analytics hold the promise of extracting insightsby uncovering difficult-to-discover patterns and connections, as well asproviding assistance in making complex decisions by analyzing differentand potentially conflicting options. As such, individuals andorganizations alike can be provided new opportunities to innovate,compete, and capture value.

One aspect of big data is “dark data,” which generally refers to datathat is either not collected, neglected, or underutilized. Examples ofdata that is not currently being collected includes location data priorto the emergence of companies such as Foursquare or social data prior tothe advent companies such as Facebook. An example of data that is beingcollected, but is difficult to access at the right time and place,includes data associated with the side effects of certain spider biteswhile on a camping trip. As another example, data that is collected andavailable, but has not yet been productized of fully utilized, mayinclude disease insights from population-wide healthcare records andsocial media feeds. As a result, a case can be made that dark data mayin fact be of higher value than big data in general, especially as itcan likely provide actionable insights when it is combined withreadily-available data.

SUMMARY OF THE INVENTION

In one embodiment, the invention relates to a cognitive informationprocessing system environment comprising a plurality of data sources; acognitive inference and learning system coupled to receive data from theplurality of data sources, the cognitive inference and learning systemprocessing the data from the plurality of data sources to perform acognitive learning operation, the cognitive learning operation applyinga cognitive learning technique to generate a cognitive learning result;and, a destination, the destination being updated based upon thelearning result.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of a cognitive inference andlearning system (CILS);

FIG. 3 is a simplified block diagram of a CILS reference modelimplemented in accordance with an embodiment of the invention;

FIGS. 4 a through 4 c depict additional components of the CILS referencemodel shown in FIG. 3 ;

FIG. 5 is a simplified process diagram of CILS operations;

FIG. 6 depicts the lifecycle of CILS agents implemented to perform CILSoperations;

FIG. 7 is a simplified block diagram of a plurality of cognitiveplatforms implemented in a hybrid cloud environment;

FIG. 8 depicts a cognitive learning framework;

FIG. 9 is a simplified block diagram of a CILS used to manage theperformance of cognitive learning operations throughout their lifecycle;and

FIGS. 10 a and 10 b are a simplified process flow diagram of theperformance of cognitive learning operations by a CILS.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for cognitiveinference and learning operations. The present invention may be asystem, a method, and/or a computer program product. The computerprogram product may include a computer readable storage medium (ormedia) having computer readable program instructions thereon for causinga processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 is a generalized illustration of an information processing system100 that can be used to implement the system and method of the presentinvention. The information processing system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, and associated controllers,a hard drive or disk storage 106, and various other subsystems 108. Invarious embodiments, the information processing system 100 also includesnetwork port 110 operable to connect to a network 140, which is likewiseaccessible by a service provider server 142. The information processingsystem 100 likewise includes system memory 112, which is interconnectedto the foregoing via one or more buses 114. System memory 112 furthercomprises operating system (OS) 116 and in various embodiments may alsocomprise cognitive inference and learning system (CILS) 118. In theseand other embodiments, the CILS 118 may likewise comprise inventionmodules 120. In one embodiment, the information processing system 100 isable to download the CILS 118 from the service provider server 142. Inanother embodiment, the CILS 118 is provided as a service from theservice provider server 142.

In various embodiments, the CILS 118 is implemented to perform variouscognitive computing operations described in greater detail herein. Asused herein, cognitive computing broadly refers to a class of computinginvolving self-learning systems that use techniques such as spatialnavigation, machine vision, and pattern recognition to increasinglymimic the way the human brain works. To be more specific, earlierapproaches to computing typically solved problems by executing a set ofinstructions codified within software. In contrast, cognitive computingapproaches are data-driven, sense-making, insight-extracting,problem-solving systems that have more in common with the structure ofthe human brain than with the architecture of contemporary,instruction-driven computers.

To further differentiate these distinctions, traditional computers mustfirst be programmed by humans to perform specific tasks, while cognitivesystems learn from their interactions with data and humans alike, and ina sense, program themselves to perform new tasks. To summarize thedifference between the two, traditional computers are designed tocalculate rapidly. Cognitive systems are designed to quickly drawinferences from data and gain new knowledge.

Cognitive systems achieve these abilities by combining various aspectsof artificial intelligence, natural language processing, dynamiclearning, and hypothesis generation to render vast quantities ofintelligible data to assist humans in making better decisions. As such,cognitive systems can be characterized as having the ability to interactnaturally with people to extend what either humans, or machines, coulddo on their own. Furthermore, they are typically able to process naturallanguage, multi-structured data, and experience much in the same way ashumans. Moreover, they are also typically able to learn a knowledgedomain based upon the best available data and get better, and moreimmersive, over time.

It will be appreciated that more data is currently being produced everyday than was recently produced by human beings from the beginning ofrecorded time. Deep within this ever-growing mass of data is a class ofdata known as “dark data,” which includes neglected information, ambientsignals, and insights that can assist organizations and individuals inaugmenting their intelligence and deliver actionable insights throughthe implementation of cognitive applications. As used herein, cognitiveapplications, or “cognitive apps,” broadly refer to cloud-based, bigdata interpretive applications that learn from user engagement and datainteractions. Such cognitive applications extract patterns and insightsfrom dark data sources that are currently almost completely opaque.Examples of such dark data include disease insights from population-widehealthcare records and social media feeds, or from new sources ofinformation, such as sensors monitoring pollution in delicate marineenvironments.

Over time, it is anticipated that cognitive applications willfundamentally change the ways in which many organizations operate asthey invert current issues associated with data volume and variety toenable a smart, interactive data supply chain. Ultimately, cognitiveapplications hold the promise of receiving a user query and immediatelyproviding a data-driven answer from a masked data supply chain inresponse. As they evolve, it is likewise anticipated that cognitiveapplications may enable a new class of “sixth sense” applications thatintelligently detect and learn from relevant data and events to offerinsights, predictions and advice rather than wait for commands. Just asweb and mobile applications changed the way people access data,cognitive applications may change the way people listen to, and becomeempowered by, multi-structured data such as emails, social media feeds,doctors notes, transaction records, and call logs.

However, the evolution of such cognitive applications has associatedchallenges, such as how to detect events, ideas, images, and othercontent that may be of interest. For example, assuming that the role andpreferences of a given user are known, how is the most relevantinformation discovered, prioritized, and summarized from large streamsof multi-structured data such as news feeds, blogs, social media,structured data, and various knowledge bases? To further the example,what can a healthcare executive be told about their competitor's marketshare? Other challenges include the creation of acontextually-appropriate visual summary of responses to questions orqueries.

FIG. 2 is a simplified block diagram of a cognitive inference andlearning system (CILS) implemented in accordance with an embodiment ofthe invention. In various embodiments, the CILS 118 is implemented toincorporate a variety of processes, including semantic analysis 202,goal optimization 204, collaborative filtering 206, common sensereasoning 208, natural language processing 210, summarization 212,temporal/spatial reasoning 214, and entity resolution 216 to generatecognitive insights.

As used herein, semantic analysis 202 broadly refers to performingvarious analysis operations to achieve a semantic level of understandingabout language by relating syntactic structures. In various embodiments,various syntactic structures are related from the levels of phrases,clauses, sentences and paragraphs, to the level of the body of contentas a whole and to its language-independent meaning. In certainembodiments, the semantic analysis 202 process includes processing atarget sentence to parse it into its individual parts of speech, tagsentence elements that are related to predetermined items of interest,identify dependencies between individual words, and perform co-referenceresolution. For example, if a sentence states that the author reallylikes the hamburgers served by a particular restaurant, then the name ofthe “particular restaurant” is co-referenced to “hamburgers.”

As likewise used herein, goal optimization 204 broadly refers toperforming multi-criteria decision making operations to achieve a givengoal or target objective. In various embodiments, one or more goaloptimization 204 processes are implemented by the CILS 118 to definepredetermined goals, which in turn contribute to the generation of acognitive insight. For example, goals for planning a vacation trip mayinclude low cost (e.g., transportation and accommodations), location(e.g., by the beach), and speed (e.g., short travel time). In thisexample, it will be appreciated that certain goals may be in conflictwith another. As a result, a cognitive insight provided by the CILS 118to a traveler may indicate that hotel accommodations by a beach may costmore than they care to spend.

Collaborative filtering 206, as used herein, broadly refers to theprocess of filtering for information or patterns through thecollaborative involvement of multiple agents, viewpoints, data sources,and so forth. The application of such collaborative filtering 206processes typically involves very large and different kinds of datasets, including sensing and monitoring data, financial data, and userdata of various kinds. Collaborative filtering 206 may also refer to theprocess of making automatic predictions associated with predeterminedinterests of a user by collecting preferences or other information frommany users. For example, if person ‘A’ has the same opinion as a person‘B’ for a given issue ‘x’, then an assertion can be made that person ‘A’is more likely to have the same opinion as person ‘B’ opinion on adifferent issue ‘y’ than to have the same opinion on issue ‘y’ as arandomly chosen person. In various embodiments, the collaborativefiltering 206 process is implemented with various recommendation enginesfamiliar to those of skill in the art to make recommendations.

As used herein, common sense reasoning 208 broadly refers to simulatingthe human ability to make deductions from common facts they inherentlyknow. Such deductions may be made from inherent knowledge about thephysical properties, purpose, intentions and possible behavior ofordinary things, such as people, animals, objects, devices, and so on.In various embodiments, common sense reasoning 208 processes areimplemented to assist the CILS 118 in understanding and disambiguatingwords within a predetermined context. In certain embodiments, the commonsense reasoning 208 processes are implemented to allow the CILS 118 togenerate text or phrases related to a target word or phrase to performdeeper searches for the same terms. It will be appreciated that if thecontext of a word is better understood, then a common senseunderstanding of the word can then be used to assist in finding betteror more accurate information. In certain embodiments, this better ormore accurate understanding of the context of a word, and its relatedinformation, allows the CILS 118 to make more accurate deductions, whichare in turn used to generate cognitive insights.

As likewise used herein, natural language processing (NLP) 210 broadlyrefers to interactions with a system, such as the CILS 118, through theuse of human, or natural, languages. In various embodiments, various NLP210 processes are implemented by the CILS 118 to achieve naturallanguage understanding, which enables it to not only derive meaning fromhuman or natural language input, but to also generate natural languageoutput.

Summarization 212, as used herein, broadly refers to processing a set ofinformation, organizing and ranking it, and then generating acorresponding summary. As an example, a news article may be processed toidentify its primary topic and associated observations, which are thenextracted, ranked, and then presented to the user. As another example,page ranking operations may be performed on the same news article toidentify individual sentences, rank them, order them, and determinewhich of the sentences are most impactful in describing the article andits content. As yet another example, a structured data record, such as apatient's electronic medical record (EMR), may be processed using thesummarization 212 process to generate sentences and phrases thatdescribes the content of the EMR. In various embodiments, varioussummarization 212 processes are implemented by the CILS 118 to generatesummarizations of content streams, which are in turn used to generatecognitive insights.

As used herein, temporal/spatial reasoning 214 broadly refers toreasoning based upon qualitative abstractions of temporal and spatialaspects of common sense knowledge, described in greater detail herein.For example, it is not uncommon for a predetermined set of data tochange over time. Likewise, other attributes, such as its associatedmetadata, may likewise change over time. As a result, these changes mayaffect the context of the data. To further the example, the context ofasking someone what they believe they should be doing at 3:00 in theafternoon during the workday while they are at work may be quitedifferent that asking the same user the same question at 3:00 on aSunday afternoon when they are at home. In various embodiments, varioustemporal/spatial reasoning 214 processes are implemented by the CILS 118to determine the context of queries, and associated data, which are inturn used to generate cognitive insights.

As likewise used herein, entity resolution 216 broadly refers to theprocess of finding elements in a set of data that refer to the sameentity across different data sources (e.g., structured, non-structured,streams, devices, etc.), where the target entity does not share a commonidentifier. In various embodiments, the entity resolution 216 process isimplemented by the CILS 118 to identify significant nouns, adjectives,phrases or sentence elements that represent various predeterminedentities within one or more domains. From the foregoing, it will beappreciated that the implementation of one or more of the semanticanalysis 202, goal optimization 204, collaborative filtering 206, commonsense reasoning 208, natural language processing 210, summarization 212,temporal/spatial reasoning 214, and entity resolution 216 processes bythe CILS 118 can facilitate the generation of a semantic, cognitivemodel.

In various embodiments, the CILS 118 receives ambient signals 220,curated data 222, and learned knowledge, which is then processed by theCILS 118 to generate one or more cognitive graphs 226. In turn, the oneor more cognitive graphs 226 are further used by the CILS 118 togenerate cognitive insight streams, which are then delivered to one ormore destinations 230, as described in greater detail herein.

As used herein, ambient signals 220 broadly refer to input signals, orother data streams, that may contain data providing additional insightor context to the curated data 222 and learned knowledge 224 received bythe CILS 118. For example, ambient signals may allow the CILS 118 tounderstand that a user is currently using their mobile device, atlocation ‘x’, at time ‘y’, doing activity ‘z’. To further the example,there is a difference between the user using their mobile device whilethey are on an airplane versus using their mobile device after landingat an airport and walking between one terminal and another. To extendthe example even further, ambient signals may add additional context,such as the user is in the middle of a three leg trip and has two hoursbefore their next flight. Further, they may be in terminal A1, but theirnext flight is out of C1, it is lunchtime, and they want to know thebest place to eat. Given the available time the user has, their currentlocation, restaurants that are proximate to their predicted route, andother factors such as food preferences, the CILS 118 can perform variouscognitive operations and provide a recommendation for where the user caneat.

In various embodiments, the curated data 222 may include structured,unstructured, social, public, private, streaming, device or other typesof data described in greater detail herein. In certain embodiments, thelearned knowledge 224 is based upon past observations and feedback fromthe presentation of prior cognitive insight streams and recommendations.In various embodiments, the learned knowledge 224 is provided via afeedback look that provides the learned knowledge 224 in the form of alearning stream of data.

As likewise used herein, a cognitive graph 226 refers to arepresentation of expert knowledge, associated with individuals andgroups over a period of time, to depict relationships between people,places, and things using words, ideas, audio and images. As such, it isa machine-readable formalism for knowledge representation that providesa common framework allowing data and knowledge to be shared and reusedacross user, application, organization, and community boundaries.

In various embodiments, the information contained in, and referenced by,a cognitive graph 226 is derived from many sources (e.g., public,private, social, device), such as curated data 222. In certain of theseembodiments, the cognitive graph 226 assists in the identification andorganization of information associated with how people, places andthings are related to one other. In various embodiments, the cognitivegraph 226 enables automated agents, described in greater detail herein,to access the Web more intelligently, enumerate inferences throughutilization of curated, structured data 222, and provide answers toquestions by serving as a computational knowledge engine.

In certain embodiments, the cognitive graph 226 not only elicits andmaps expert knowledge by deriving associations from data, it alsorenders higher level insights and accounts for knowledge creationthrough collaborative knowledge modeling. In various embodiments, thecognitive graph 226 is a machine-readable, declarative memory systemthat stores and learns both episodic memory (e.g., specific personalexperiences associated with an individual or entity), and semanticmemory, which stores factual information (e.g., geo location of anairport or restaurant).

For example, the cognitive graph 226 may know that a given airport is aplace, and that there is a list of related places such as hotels,restaurants and departure gates. Furthermore, the cognitive graph 226may know that people such as business travelers, families and collegestudents use the airport to board flights from various carriers, eat atvarious restaurants, or shop at certain retail stores. The cognitivegraph 226 may also have knowledge about the key attributes from variousretail rating sites that travelers have used to describe the food andtheir experience at various venues in the airport over the past sixmonths.

In certain embodiments, the cognitive insight stream 228 isbidirectional, and supports flows of information both too and fromdestinations 230. In these embodiments, the first flow is generated inresponse to receiving a query, and subsequently delivered to one or moredestinations 230. The second flow is generated in response to detectinginformation about a user of one or more of the destinations 230. Suchuse results in the provision of information to the CILS 118. Inresponse, the CILS 118 processes that information, in the context ofwhat it knows about the user, and provides additional information to theuser, such as a recommendation. In various embodiments, the cognitiveinsight stream 228 is configured to be provided in a “push” streamconfiguration familiar to those of skill in the art. In certainembodiments, the cognitive insight stream 228 is implemented to usenatural language approaches familiar to skilled practitioners of the artto support interactions with a user.

In various embodiments, the cognitive insight stream 228 may include astream of visualized insights. As used herein, visualized insightsbroadly refers to cognitive insights that are presented in a visualmanner, such as a map, an infographic, images, and so forth. In certainembodiments, these visualized insights may include various cognitiveinsights, such as “What happened?”, “What do I know about it?”, “What islikely to happen next?”, or “What should I do about it?” In theseembodiments, the cognitive insight stream is generated by variouscognitive agents, which are applied to various sources, datasets, andcognitive graphs. As used herein, a cognitive agent broadly refers to acomputer program that performs a task with minimum specific directionsfrom users and learns from each interaction with data and human users.

In various embodiments, the CILS 118 delivers Cognition as a Service(CaaS). As such, it provides a cloud-based development and executionplatform that allow various cognitive applications and services tofunction more intelligently and intuitively. In certain embodiments,cognitive applications powered by the CILS 118 are able to think andinteract with users as intelligent virtual assistants. As a result,users are able to interact with such cognitive applications by askingthem questions and giving them commands. In response, these cognitiveapplications will be able to assist the user in completing tasks andmanaging their work more efficiently.

In these and other embodiments, the CILS 118 can operate as an analyticsplatform to process big data, and dark data as well, to provide dataanalytics through a public, private or hybrid cloud environment. As usedherein, cloud analytics broadly refers to a service model wherein datasources, data models, processing applications, computing power, analyticmodels, and sharing or storage of results are implemented within a cloudenvironment to perform one or more aspects of analytics.

In various embodiments, users submit queries and computation requests ina natural language format to the CILS 118. In response, they areprovided with a ranked list of relevant answers and aggregatedinformation with useful links and pertinent visualizations through agraphical representation. In these embodiments, the cognitive graph 226generates semantic and temporal maps to reflect the organization ofunstructured data and to facilitate meaningful learning from potentiallymillions of lines of text, much in the same way as arbitrary syllablesstrung together create meaning through the concept of language.

FIG. 3 is a simplified block diagram of a cognitive inference andlearning system (CILS) reference model implemented in accordance with anembodiment of the invention. In this embodiment, the CILS referencemodel is associated with the CILS 118 shown in FIG. 2 . As shown in FIG.3 , the CILS 118 includes client applications 302, applicationaccelerators 306, a cognitive platform 310, and cloud infrastructure340. In various embodiments, the client applications 302 includecognitive applications 304, which are implemented to understand andadapt to the user, not the other way around, by natively accepting andunderstanding human forms of communication, such as natural languagetext, audio, images, video, and so forth.

In these and other embodiments, the cognitive applications 304 possesssituational and temporal awareness based upon ambient signals from usersand data, which facilitates understanding the user's intent, content,context and meaning to drive goal-driven dialogs and outcomes. Further,they are designed to gain knowledge over time from a wide variety ofstructured, non-structured, and device data sources, continuouslyinterpreting and autonomously reprogramming themselves to betterunderstand a given domain. As such, they are well-suited to supporthuman decision making, by proactively providing trusted advice, offersand recommendations while respecting user privacy and permissions.

In various embodiments, the application accelerators 306 include acognitive application framework 308. In certain embodiments, theapplication accelerators 306 and the cognitive application framework 308support various plug-ins and components that facilitate the creation ofclient applications 302 and cognitive applications 304. In variousembodiments, the application accelerators 306 include widgets, userinterface (UI) components, reports, charts, and back-end integrationcomponents familiar to those of skill in the art.

As likewise shown in FIG. 3 , the cognitive platform 310 includes amanagement console 312, a development environment 314, applicationprogram interfaces (APIs) 316, sourcing agents 318, a cognitive engine320, destination agents 336, and platform data 338, all of which aredescribed in greater detail herein. In various embodiments, themanagement console 312 is implemented to manage accounts and projects,along with user-specific metadata that is used to drive processes andoperations within the cognitive platform 310 for a predeterminedproject.

In certain embodiments, the development environment 314 is implementedto create custom extensions to the CILS 118 shown in FIG. 2 . In variousembodiments, the development environment 314 is implemented for thedevelopment of a custom application, which may subsequently be deployedin a public, private or hybrid cloud environment. In certainembodiments, the development environment 314 is implemented for thedevelopment of a custom sourcing agent, a custom bridging agent, acustom destination agent, or various analytics applications orextensions.

In various embodiments, the APIs 316 are implemented to build and managepredetermined cognitive applications 304, described in greater detailherein, which are then executed on the cognitive platform 310 togenerate cognitive insights. Likewise, the sourcing agents 318 areimplemented in various embodiments to source a variety of multi-site,multi-structured source streams of data described in greater detailherein. In various embodiments, the cognitive engine 320 includes adataset engine 322, a graph query engine 326, an insight/learning engine330, and foundation components 334. In certain embodiments, the datasetengine 322 is implemented to establish and maintain a dynamic dataingestion and enrichment pipeline. In these and other embodiments, thedataset engine 322 may be implemented to orchestrate one or moresourcing agents 318 to source data. Once the data is sourced, the dataset engine 322 performs data enriching and other data processingoperations, described in greater detail herein, and generates one ormore sub-graphs that are subsequently incorporated into a targetcognitive graph.

In various embodiments, the graph query engine 326 is implemented toreceive and process queries such that they can be bridged into acognitive graph, as described in greater detail herein, through the useof a bridging agent. In certain embodiments, the graph query engine 326performs various natural language processing (NLP), familiar to skilledpractitioners of the art, to process the queries. In variousembodiments, the insight/learning engine 330 is implemented toencapsulate a predetermined algorithm, which is then applied to acognitive graph to generate a result, such as a cognitive insight or arecommendation. In certain embodiments, one or more such algorithms maycontribute to answering a specific question and provide additionalcognitive insights or recommendations. In various embodiments, two ormore of the dataset engine 322, the graph query engine 326, and theinsight/learning engine 330 may be implemented to operatecollaboratively to generate a cognitive insight or recommendation. Incertain embodiments, one or more of the dataset engine 322, the graphquery engine 326, and the insight/learning engine 330 may operateautonomously to generate a cognitive insight or recommendation.

The foundation components 334 shown in FIG. 3 include various reusablecomponents, familiar to those of skill in the art, which are used invarious embodiments to enable the dataset engine 322, the graph queryengine 326, and the insight/learning engine 330 to perform theirrespective operations and processes. Examples of such foundationcomponents 334 include natural language processing (NLP) components andcore algorithms, such as cognitive algorithms.

In various embodiments, the platform data 338 includes various datarepositories, described in greater detail herein, that are accessed bythe cognitive platform 310 to generate cognitive insights. In variousembodiments, the destination agents 336 are implemented to publishcognitive insights to a consumer of cognitive insight data. Examples ofsuch consumers of cognitive insight data include target databases,business intelligence applications, and mobile applications. It will beappreciated that many such examples of cognitive insight data consumersare possible and the foregoing is not intended to limit the spirit,scope or intent of the invention. In various embodiments, as describedin greater detail herein, the cloud infrastructure 340 includescognitive cloud management 342 components and cloud analyticsinfrastructure components 344.

FIGS. 4 a through 4 c depict additional cognitive inference and learningsystem (CILS) components implemented in accordance with an embodiment ofthe CILS reference model shown in FIG. 3 . In this embodiment, the CILSreference model includes client applications 302, applicationaccelerators 306, a cognitive platform 310, and cloud infrastructure340. As shown in FIG. 4 a , the client applications 302 includecognitive applications 304. In various embodiments, the cognitiveapplications 304 are implemented natively accept and understand humanforms of communication, such as natural language text, audio, images,video, and so forth. In certain embodiments, the cognitive applications304 may include healthcare 402, business performance 403, travel 404,and various other 405 applications familiar to skilled practitioners ofthe art. As such, the foregoing is only provided as examples of suchcognitive applications 304 and is not intended to limit the intent,spirit of scope of the invention.

In various embodiments, the application accelerators 306 include acognitive application framework 308. In certain embodiments, theapplication accelerators 308 and the cognitive application framework 308support various plug-ins and components that facilitate the creation ofclient applications 302 and cognitive applications 304. In variousembodiments, the application accelerators 306 include widgets, userinterface (UI) components, reports, charts, and back-end integrationcomponents familiar to those of skill in the art. It will be appreciatedthat many such application accelerators 306 are possible and theirprovided functionality, selection, provision and support are a matter ofdesign choice. As such, the application accelerators 306 described ingreater detail herein are not intended to limit the spirit, scope orintent of the invention.

As shown in FIGS. 4 a and 4 b , the cognitive platform 310 includes amanagement console 312, a development environment 314, applicationprogram interfaces (APIs) 316, sourcing agents 318, a cognitive engine320, destination agents 336, platform data 338, and a crawl framework452. In various embodiments, the management console 312 is implementedto manage accounts and projects, along with management metadata 461 thatis used to drive processes and operations within the cognitive platform310 for a predetermined project.

In various embodiments, the management console 312 is implemented to runvarious services on the cognitive platform 310. In certain embodiments,the management console 312 is implemented to manage the configuration ofthe cognitive platform 310. In certain embodiments, the managementconsole 312 is implemented to establish the development environment 314.In various embodiments, the management console 312 may be implemented tomanage the development environment 314 once it is established. Skilledpractitioners of the art will realize that many such embodiments arepossible and the foregoing is not intended to limit the spirit, scope orintent of the invention.

In various embodiments, the development environment 314 is implementedto create custom extensions to the CILS 118 shown in FIG. 2 . In theseand other embodiments, the development environment 314 is implemented tosupport various programming languages, such as Python, Java, R, andothers familiar to skilled practitioners of the art. In variousembodiments, the development environment 314 is implemented to allow oneor more of these various programming languages to create a variety ofanalytic models and applications. As an example, the developmentenvironment 314 may be implemented to support the R programminglanguage, which in turn can be used to create an analytic model that isthen hosted on the cognitive platform 310.

In certain embodiments, the development environment 314 is implementedfor the development of various custom applications or extensions relatedto the cognitive platform 310, which may subsequently be deployed in apublic, private or hybrid cloud environment. In various embodiments, thedevelopment environment 314 is implemented for the development ofvarious custom sourcing agents 318, custom enrichment agents 425, custombridging agents 429, custom insight agents 433, custom destinationagents 336, and custom learning agents 434, which are described ingreater detail herein.

In various embodiments, the APIs 316 are implemented to build and managepredetermined cognitive applications 304, described in greater detailherein, which are then executed on the cognitive platform 310 togenerate cognitive insights. In these embodiments, the APIs 316 mayinclude one or more of a project and dataset API 408, a cognitive searchAPI 409, a cognitive insight API 410, and other APIs. The selection ofthe individual APIs 316 implemented in various embodiments is a matterdesign choice and the foregoing is not intended to limit the spirit,scope or intent of the invention.

In various embodiments, the project and dataset API 408 is implementedwith the management console 312 to enable the management of a variety ofdata and metadata associated with various cognitive insight projects anduser accounts hosted or supported by the cognitive platform 310. In oneembodiment, the data and metadata managed by the project and dataset API408 are associated with billing information familiar to those of skillin the art. In one embodiment, the project and dataset API 408 is usedto access a data stream that is created, configured and orchestrated, asdescribed in greater detail herein, by the dataset engine 322.

In various embodiments, the cognitive search API 409 uses naturallanguage processes familiar to those of skill in the art to search atarget cognitive graph. Likewise, the cognitive insight API 410 isimplemented in various embodiments to configure the insight/learningengine 330 to provide access to predetermined outputs from one or morecognitive graph algorithms that are executing in the cognitive platform310. In certain embodiments, the cognitive insight API 410 isimplemented to subscribe to, or request, such predetermined outputs.

In various embodiments, the sourcing agents 318 may include a batchupload 414 agent, an API connectors 415 agent, a real-time streams 416agent, a Structured Query Language (SQL)/Not Only SQL (NoSQL) databases417 agent, a message engines 418 agent, and one or more custom sourcing420 agents. Skilled practitioners of the art will realize that othertypes of sourcing agents 318 may be used in various embodiments and theforegoing is not intended to limit the spirit, scope or intent of theinvention. In various embodiments, the sourcing agents 318 areimplemented to source a variety of multi-site, multi-structured sourcestreams of data described in greater detail herein. In certainembodiments, each of the sourcing agents 318 has a corresponding API.

In various embodiments, the batch uploading 414 agent is implemented forbatch uploading of data to the cognitive platform 310. In theseembodiments, the uploaded data may include a single data element, asingle data record or file, or a plurality of data records or files. Incertain embodiments, the data may be uploaded from more than one sourceand the uploaded data may be in a homogenous or heterogeneous form. Invarious embodiments, the API connectors 415 agent is implemented tomanage interactions with one or more predetermined APIs that areexternal to the cognitive platform 310. As an example, Associated Press®may have their own API for news stories, Expedia® for travelinformation, or the National Weather Service for weather information. Inthese examples, the API connectors 415 agent would be implemented todetermine how to respectively interact with each organization's API suchthat the cognitive platform 310 can receive information.

In various embodiments, the real-time streams 416 agent is implementedto receive various streams of data, such as social media streams (e.g.,Twitter feeds) or other data streams (e.g., device data streams). Inthese embodiments, the streams of data are received in near-real-time.In certain embodiments, the data streams include temporal attributes. Asan example, as data is added to a blog file, it is time-stamped tocreate temporal data. Other examples of a temporal data stream includeTwitter feeds, stock ticker streams, device location streams from adevice that is tracking location, medical devices tracking a patient'svital signs, and intelligent thermostats used to improve energyefficiency for homes.

In certain embodiments, the temporal attributes define a time window,which can be correlated to various elements of data contained in thestream. For example, as a given time window changes, associated data mayhave a corresponding change. In various embodiments, the temporalattributes do not define a time window. As an example, a social mediafeed may not have predetermined time windows, yet it is still temporal.As a result, the social media feed can be processed to determine whathappened in the last 24 hours, what happened in the last hour, whathappened in the last 15 minutes, and then determine related subjectmatter that is trending.

In various embodiments, the SQL/NoSQL databases 417 agent is implementedto interact with one or more target databases familiar to those of skillin the art. For example, the target database may include a SQL, NoSQL,delimited flat file, or other form of database. In various embodiments,the message engines 418 agent is implemented to provide data to thecognitive platform 310 from one or more message engines, such as amessage queue (MQ) system, a message bus, a message broker, anenterprise service bus (ESB), and so forth. Skilled practitioners of theart will realize that there are many such examples of message engineswith which the message engines 418 agent may interact and the foregoingis not intended to limit the spirit, scope or intent of the invention.

In various embodiments, the custom sourcing agents 420, which arepurpose-built, are developed through the use of the developmentenvironment 314, described in greater detail herein. Examples of customsourcing agents 420 include sourcing agents for various electronicmedical record (EMR) systems at various healthcare facilities. Such EMRsystems typically collect a variety of healthcare information, much ofit the same, yet it may be collected, stored and provided in differentways. In this example, the custom sourcing agents 420 allow thecognitive platform 310 to receive information from each disparatehealthcare source.

In various embodiments, the cognitive engine 320 includes a datasetengine 322, a graph engine 326, an insight/learning engine 330, learningagents 434, and foundation components 334. In these and otherembodiments, the dataset engine 322 is implemented as described ingreater detail to establish and maintain a dynamic data ingestion andenrichment pipeline. In various embodiments, the dataset engine 322 mayinclude a pipelines 422 component, an enrichment 423 component, astorage component 424, and one or more enrichment agents 425.

In various embodiments, the pipelines 422 component is implemented toingest various data provided by the sourcing agents 318. Once ingested,this data is converted by the pipelines 422 component into streams ofdata for processing. In certain embodiments, these managed streams areprovided to the enrichment 423 component, which performs data enrichmentoperations familiar to those of skill in the art. As an example, a datastream may be sourced from Associated Press® by a sourcing agent 318 andprovided to the dataset engine 322. The pipelines 422 component receivesthe data stream and routes it to the enrichment 423 component, whichthen enriches the data stream by performing sentiment analysis,geotagging, and entity detection operations to generate an enriched datastream. In certain embodiments, the enrichment operations includefiltering operations familiar to skilled practitioners of the art. Tofurther the preceding example, the Associated Press® data stream may befiltered by a predetermined geography attribute to generate an enricheddata stream.

The enriched data stream is then subsequently stored, as described ingreater detail herein, in a predetermined location. In variousembodiments, the enriched data stream is cached by the storage 424component to provide a local version of the enriched data stream. Incertain embodiments, the cached, enriched data stream is implemented tobe “replayed” by the cognitive engine 320. In one embodiment, thereplaying of the cached, enriched data stream allows incrementalingestion of the enriched data stream instead of ingesting the entireenriched data stream at one time. In various embodiments, one or moreenrichment agents 425 are implemented to be invoked by the enrichmentcomponent 423 to perform one or more enrichment operations described ingreater detail herein.

In various embodiments, the graph query engine 326 is implemented toreceive and process queries such that they can be bridged into acognitive graph, as described in greater detail herein, through the useof a bridging agent. In these embodiments, the graph query engine mayinclude a query 426 component, a translate 427 component, a bridge 428component, and one or more bridging agents 429.

In various embodiments, the query 426 component is implemented tosupport natural language queries. In these and other embodiments, thequery 426 component receives queries, processes them (e.g., using NLPprocesses), and then maps the processed query to a target cognitivegraph. In various embodiments, the translate 427 component isimplemented to convert the processed queries provided by the query 426component into a form that can be used to query a target cognitivegraph. To further differentiate the distinction between thefunctionality respectively provided by the query 426 and translate 427components, the query 426 component is oriented toward understanding aquery from a user. In contrast, the translate 427 component is orientedto translating a query that is understood into a form that can be usedto query a cognitive graph.

In various embodiments, the bridge 428 component is implemented togenerate an answer to a query provided by the translate 427 component.In certain embodiments, the bridge 428 component is implemented toprovide domain-specific responses when bridging a translated query to acognitive graph. For example, the same query bridged to a targetcognitive graph by the bridge 428 component may result in differentanswers for different domains, dependent upon domain-specific bridgingoperations performed by the bridge 428 component.

To further differentiate the distinction between the translate 427component and the bridging 428 component, the translate 427 componentrelates to a general domain translation of a question. In contrast, thebridging 428 component allows the question to be asked in the context ofa specific domain (e.g., healthcare, travel, etc.), given what is knownabout the data. In certain embodiments, the bridging 428 component isimplemented to process what is known about the translated query, in thecontext of the user, to provide an answer that is relevant to a specificdomain.

As an example, a user may ask, “Where should I eat today?” If the userhas been prescribed a particular health regimen, the bridging 428component may suggest a restaurant with a “heart healthy” menu. However,if the user is a business traveler, the bridging 428 component maysuggest the nearest restaurant that has the user's favorite food. Invarious embodiments, the bridging 428 component may provide answers, orsuggestions, that are composed and ranked according to a specific domainof use. In various embodiments, the bridging agent 429 is implemented tointeract with the bridging component 428 to perform bridging operationsdescribed in greater detail herein. In these embodiments, the bridgingagent interprets a translated query generated by the query 426 componentwithin a predetermined user context, and then maps it to predeterminednodes and links within a target cognitive graph.

In various embodiments, the insight/learning engine 330 is implementedto encapsulate a predetermined algorithm, which is then applied to atarget cognitive graph to generate a result, such as a cognitive insightor a recommendation. In certain embodiments, one or more such algorithmsmay contribute to answering a specific question and provide additionalcognitive insights or recommendations. In these and other embodiments,the insight/learning engine 330 is implemented to performinsight/learning operations, described in greater detail herein. Invarious embodiments, the insight/learning engine 330 may include adiscover/visibility 430 component, a predict 431 component, arank/recommend 432 component, and one or more insight 433 agents.

In various embodiments, the discover/visibility 430 component isimplemented to provide detailed information related to a predeterminedtopic, such as a subject or an event, along with associated historicalinformation. In certain embodiments, the predict 431 component isimplemented to perform predictive operations to provide insight intowhat may next occur for a predetermined topic. In various embodiments,the rank/recommend 432 component is implemented to perform ranking andrecommendation operations to provide a user prioritized recommendationsassociated with a provided cognitive insight.

In certain embodiments, the insight/learning engine 330 may includeadditional components. For example the additional components may includeclassification algorithms, clustering algorithms, and so forth. Skilledpractitioners of the art will realize that many such additionalcomponents are possible and that the foregoing is not intended to limitthe spirit, scope or intent of the invention. In various embodiments,the insights agents 433 are implemented to create a visual data story,highlighting user-specific insights, relationships and recommendations.As a result, it can share, operationalize, or track business insights invarious embodiments. In various embodiments, the learning agent 434 workin the background to continually update the cognitive graph, asdescribed in greater detail herein, from each unique interaction withdata and users.

In various embodiments, the destination agents 336 are implemented topublish cognitive insights to a consumer of cognitive insight data.Examples of such consumers of cognitive insight data include targetdatabases, business intelligence applications, and mobile applications.In various embodiments, the destination agents 336 may include aHypertext Transfer Protocol (HTTP) stream 440 agent, an API connectors441 agent, a databases 442 agent, a message engines 443 agent, a mobilepush notification 444 agent, and one or more custom destination 446agents. Skilled practitioners of the art will realize that other typesof destination agents 318 may be used in various embodiments and theforegoing is not intended to limit the spirit, scope or intent of theinvention. In certain embodiments, each of the destination agents 318has a corresponding API.

In various embodiments, the HTTP stream 440 agent is implemented forproviding various HTTP streams of cognitive insight data to apredetermined cognitive data consumer. In these embodiments, theprovided HTTP streams may include various HTTP data elements familiar tothose of skill in the art. In certain embodiments, the HTTP streams ofdata are provided in near-real-time. In various embodiments, the APIconnectors 441 agent is implemented to manage interactions with one ormore predetermined APIs that are external to the cognitive platform 310.As an example, various target databases, business intelligenceapplications, and mobile applications may each have their own uniqueAPI.

In various embodiments, the databases 442 agent is implemented forprovision of cognitive insight data to one or more target databasesfamiliar to those of skill in the art. For example, the target databasemay include a SQL, NoSQL, delimited flat file, or other form ofdatabase. In these embodiments, the provided cognitive insight data mayinclude a single data element, a single data record or file, or aplurality of data records or files. In certain embodiments, the data maybe provided to more than one cognitive data consumer and the provideddata may be in a homogenous or heterogeneous form. In variousembodiments, the message engines 443 agent is implemented to providecognitive insight data to one or more message engines, such as a messagequeue (MQ) system, a message bus, a message broker, an enterpriseservice bus (ESB), and so forth. Skilled practitioners of the art willrealize that there are many such examples of message engines with whichthe message engines 443 agent may interact and the foregoing is notintended to limit the spirit, scope or intent of the invention.

In various embodiments, the custom destination agents 420, which arepurpose-built, are developed through the use of the developmentenvironment 314, described in greater detail herein. Examples of customdestination agents 420 include destination agents for various electronicmedical record (EMR) systems at various healthcare facilities. Such EMRsystems typically collect a variety of healthcare information, much ofit the same, yet it may be collected, stored and provided in differentways. In this example, the custom destination agents 420 allow such EMRsystems to receive cognitive insight data in a form they can use.

In various embodiments, data that has been cleansed, normalized andenriched by the dataset engine, as described in greater detail herein,is provided by a destination agent 336 to a predetermined destination,likewise described in greater detail herein. In these embodiments,neither the graph query engine 326 nor the insight/learning engine 330are implemented to perform their respective functions.

In various embodiments, the foundation components 334 are implemented toenable the dataset engine 322, the graph query engine 326, and theinsight/learning engine 330 to perform their respective operations andprocesses. In these and other embodiments, the foundation components 334may include an NLP core 436 component, an NLP services 437 component,and a dynamic pipeline engine 438. In various embodiments, the NLP core436 component is implemented to provide a set of predetermined NLPcomponents for performing various NLP operations described in greaterdetail herein.

In these embodiments, certain of these NLP core components are surfacedthrough the NLP services 437 component, while some are used aslibraries. Examples of operations that are performed with suchcomponents include dependency parsing, parts-of-speech tagging, sentencepattern detection, and so forth. In various embodiments, the NLPservices 437 component is implemented to provide various internal NLPservices, which are used to perform entity detection, summarization, andother operations, likewise described in greater detail herein. In theseembodiments, the NLP services 437 component is implemented to interactwith the NLP core 436 component to provide predetermined NLP services,such as summarizing a target paragraph.

In various embodiments, the dynamic pipeline engine 438 is implementedto interact with the dataset engine 322 to perform various operationsrelated to receiving one or more sets of data from one or more sourcingagents, apply enrichment to the data, and then provide the enriched datato a predetermined destination. In these and other embodiments, thedynamic pipeline engine 438 manages the distribution of these variousoperations to a predetermined compute cluster and tracks versioning ofthe data as it is processed across various distributed computingresources. In certain embodiments, the dynamic pipeline engine 438 isimplemented to perform data sovereignty management operations tomaintain sovereignty of the data.

In various embodiments, the platform data 338 includes various datarepositories, described in greater detail herein, that are accessed bythe cognitive platform 310 to generate cognitive insights. In theseembodiments, the platform data 338 repositories may include repositoriesof dataset metadata 456, cognitive graphs 457, models 459, crawl data460, and management metadata 461. In various embodiments, the datasetmetadata 456 is associated with curated data 458 contained in therepository of cognitive graphs 457. In these and other embodiments, therepository of dataset metadata 456 contains dataset metadata thatsupports operations performed by the storage 424 component of thedataset engine 322. For example, if a Mongo® NoSQL database with tenmillion items is being processed, and the cognitive platform 310 failsafter ingesting nine million of the items, then the dataset metadata 456may be able to provide a checkpoint that allows ingestion to continue atthe point of failure instead restarting the ingestion process.

Those of skill in the art will realize that the use of such datasetmetadata 456 in various embodiments allows the dataset engine 322 to bestateful. In certain embodiments, the dataset metadata 456 allowssupport of versioning. For example versioning may be used to trackversions of modifications made to data, such as in data enrichmentprocesses described in greater detail herein. As another example,geotagging information may have been applied to a set of data during afirst enrichment process, which creates a first version of enricheddata. Adding sentiment data to the same million records during a secondenrichment process creates a second version of enriched data. In thisexample, the dataset metadata stored in the dataset metadata 456provides tracking of the different versions of the enriched data and thedifferences between the two.

In various embodiments, the repository of cognitive graphs 457 isimplemented to store cognitive graphs generated, accessed, and updatedby the cognitive engine 320 in the process of generating cognitiveinsights. In various embodiments, the repository of cognitive graphs 457may include one or more repositories of curated data 458, described ingreater detail herein. In certain embodiments, the repositories ofcurated data 458 includes data that has been curated by one or moreusers, machine operations, or a combination of the two, by performingvarious sourcing, filtering, and enriching operations described ingreater detail herein. In these and other embodiments, the curated data458 is ingested by the cognitive platform 310 and then processed, aslikewise described in greater detail herein, to generate cognitiveinsights. In various embodiments, the repository of models 459 isimplemented to store models that are generated, accessed, and updated bythe cognitive engine 320 in the process of generating cognitiveinsights. As used herein, models broadly refer to machine learningmodels. In certain embodiments, the models include one or morestatistical models.

In various embodiments, the crawl framework 452 is implemented tosupport various crawlers 454 familiar to skilled practitioners of theart. In certain embodiments, the crawlers 454 are custom configured forvarious target domains. For example, different crawlers 454 may be usedfor various travel forums, travel blogs, travel news and other travelsites. In various embodiments, data collected by the crawlers 454 isprovided by the crawl framework 452 to the repository of crawl data 460.In these embodiments, the collected crawl data is processed and thenstored in a normalized form in the repository of crawl data 460. Thenormalized data is then provided to SQL/NoSQL database 417 agent, whichin turn provides it to the dataset engine 322. In one embodiment, thecrawl database 460 is a NoSQL database, such as Mongo®.

In various embodiments, the repository of management metadata 461 isimplemented to store user-specific metadata used by the managementconsole 312 to manage accounts (e.g., billing information) and projects.In certain embodiments, the user-specific metadata stored in therepository of management metadata 461 is used by the management console312 to drive processes and operations within the cognitive platform 310for a predetermined project. In various embodiments, the user-specificmetadata stored in the repository of management metadata 461 is used toenforce data sovereignty. It will be appreciated that many suchembodiments are possible and the foregoing is not intended to limit thespirit, scope or intent of the invention.

Referring now to FIG. 4 c , the cloud infrastructure 340 may include acognitive cloud management 342 component and a cloud analyticsinfrastructure 344 component in various embodiments. Current examples ofa cloud infrastructure 340 include Amazon Web Services (AWS®), availablefrom Amazon.com® of Seattle, Wash., IBM® Softlayer, available fromInternational Business Machines of Armonk, N.Y., and Nebula/Openstack, ajoint project between Raskspace Hosting®, of Windcrest, Tex., and theNational Aeronautics and Space Administration (NASA). In theseembodiments, the cognitive cloud management 342 component may include amanagement playbooks 468 sub-component, a cognitive cloud managementconsole 469 sub-component, a data console 470 sub-component, an assetrepository 471 sub-component. In certain embodiments, the cognitivecloud management 342 component may include various other sub-components.

In various embodiments, the management playbooks 468 sub-component isimplemented to automate the creation and management of the cloudanalytics infrastructure 344 component along with various otheroperations and processes related to the cloud infrastructure 340. Asused herein, “management playbooks” broadly refers to any set ofinstructions or data, such as scripts and configuration data, that isimplemented by the management playbooks 468 sub-component to perform itsassociated operations and processes.

In various embodiments, the cognitive cloud management console 469sub-component is implemented to provide a user visibility and managementcontrols related to the cloud analytics infrastructure 344 componentalong with various other operations and processes related to the cloudinfrastructure 340. In various embodiments, the data console 470sub-component is implemented to manage platform data 338, described ingreater detail herein. In various embodiments, the asset repository 471sub-component is implemented to provide access to various cognitivecloud infrastructure assets, such as asset configurations, machineimages, and cognitive insight stack configurations.

In various embodiments, the cloud analytics infrastructure 344 componentmay include a data grid 472 sub-component, a distributed compute engine474 sub-component, and a compute cluster management 476 sub-component.In these embodiments, the cloud analytics infrastructure 344 componentmay also include a distributed object storage 478 sub-component, adistributed full text search 480 sub-component, a document database 482sub-component, a graph database 484 sub-component, and various othersub-components. In various embodiments, the data grid 472 sub-componentis implemented to provide distributed and shared memory that allows thesharing of objects across various data structures. One example of a datagrid 472 sub-component is Redis, an open-source, networked, in-memory,key-value data store, with optional durability, written in ANSI C. Invarious embodiments, the distributed compute engine 474 sub-component isimplemented to allow the cognitive platform 310 to perform variouscognitive insight operations and processes in a distributed computingenvironment. Examples of such cognitive insight operations and processesinclude batch operations and streaming analytics processes.

In various embodiments, the compute cluster management 476 sub-componentis implemented to manage various computing resources as a computecluster. One such example of such a compute cluster management 476sub-component is Mesos/Nimbus, a cluster management platform thatmanages distributed hardware resources into a single pool of resourcesthat can be used by application frameworks to efficiently manageworkload distribution for both batch jobs and long-running services. Invarious embodiments, the distributed object storage 478 sub-component isimplemented to manage the physical storage and retrieval of distributedobjects (e.g., binary file, image, text, etc.) in a cloud environment.Examples of a distributed object storage 478 sub-component includeAmazon S3®, available from Amazon.com of Seattle, Wash., and Swift, anopen source, scalable and redundant storage system.

In various embodiments, the distributed full text search 480sub-component is implemented to perform various full text searchoperations familiar to those of skill in the art within a cloudenvironment. In various embodiments, the document database 482sub-component is implemented to manage the physical storage andretrieval of structured data in a cloud environment. Examples of suchstructured data include social, public, private, and device data, asdescribed in greater detail herein. In certain embodiments, thestructured data includes data that is implemented in the JavaScriptObject Notation (JSON) format. One example of a document database 482sub-component is Mongo, an open source cross-platform document-orienteddatabase. In various embodiments, the graph database 484 sub-componentis implemented to manage the physical storage and retrieval of cognitivegraphs. One example of a graph database 484 sub-component is GraphDB, anopen source graph database familiar to those of skill in the art.

FIG. 5 is a simplified process diagram of cognitive inference andlearning system (CILS) operations performed in accordance with anembodiment of the invention. In various embodiments, these CILSoperations may include a perceive 506 phase, a relate 508 phase, anoperate 510 phase, a process and execute 512 phase, and a learn 514phase. In these and other embodiments, the CILS 118 shown in FIG. 2 isimplemented to mimic cognitive processes associated with the humanbrain. In various embodiments, the CILS operations are performed throughthe implementation of a cognitive platform 310, described in greaterdetail herein. In these and other embodiments, the cognitive platform310 may be implemented within a cloud analytics infrastructure 344,which in turn is implemented within a cloud infrastructure 340, likewisedescribed in greater detail herein.

In various embodiments, multi-site, multi-structured source streams 504are provided by sourcing agents, as described in greater detail herein.In these embodiments, the source streams 504 are dynamically ingested inreal-time during the perceive 506 phase, and based upon a predeterminedcontext, extraction, parsing, and tagging operations are performed onlanguage, text and images contained in the source streams 504. Automaticfeature extraction and modeling operations are then performed with thepreviously processed source streams 504 during the relate 508 phase togenerate queries to identify related data (i.e., corpus expansion).

In various embodiments, operations are performed during the operate 510phase to discover, summarize and prioritize various concepts, which arein turn used to generate actionable recommendations and notificationsassociated with predetermined plan-based optimization goals. Theresulting actionable recommendations and notifications are thenprocessed during the process and execute 512 phase to provide cognitiveinsights, such as recommendations, to various predetermined destinationsand associated application programming interfaces (APIs) 524.

In various embodiments, features from newly-observed data areautomatically extracted from user feedback during the learn 514 phase toimprove various analytical models. In these embodiments, the learn 514phase includes feedback on observations generated during the relate 508phase, which is provided to the perceive 506 phase. Likewise, feedbackon decisions resulting from operations performed during the operate 510phase, and feedback on results resulting from operations performedduring the process and execute 512 phase, are also provided to theperceive 506 phase.

In various embodiments, user interactions result from operationsperformed during the process and execute 512 phase. In theseembodiments, data associated with the user interactions are provided tothe perceive 506 phase as unfolding interactions 522, which includeevents that occur external to the CILS operations described in greaterdetail herein. As an example, a first query from a user may be submittedto the CILS system, which in turn generates a first cognitive insight,which is then provided to the user. In response, the user may respond byproviding a first response, or perhaps a second query, either of whichis provided in the same context as the first query. The CILS receivesthe first response or second query, performs various CILS operations,and provides the user a second cognitive insight. As before, the usermay respond with a second response or a third query, again in thecontext of the first query. Once again, the CILS performs various CILSoperations and provides the user a third cognitive insight, and soforth. In this example, the provision of cognitive insights to the user,and their various associated responses, results in unfoldinginteractions 522, which in turn result in a stateful dialog that evolvesover time. Skilled practitioners of the art will likewise realize thatsuch unfolding interactions 522, occur outside of the CILS operationsperformed by the cognitive platform 310.

FIG. 6 depicts the lifecycle of CILS agents implemented in accordancewith an embodiment of the invention to perform CILS operations. Invarious embodiments, the CILS agents lifecycle 602 may includeimplementation of a sourcing 318 agent, an enrichment 425 agent, abridging 429 agent, an insight 433 agent, a destination 336 agent, and alearning 434 agent. In these embodiments, the sourcing 318 agent isimplemented to source a variety of multi-site, multi-structured sourcestreams of data described in greater detail herein. These sourced datastreams are then provided to an enrichment 425 agent, which then invokesan enrichment component to perform enrichment operations to generateenriched data streams, likewise described in greater detail herein.

The enriched data streams are then provided to a bridging 429 agent,which is used to perform bridging operations described in greater detailherein. In turn, the results of the bridging operations are provided toan insight 433 agent, which is implemented as described in greaterdetail herein to create a visual data story, highlighting user-specificinsights, relationships and recommendations. The resulting visual datastory is then provided to a destination 336 agent, which is implementedto publish cognitive insights to a consumer of cognitive insight data,likewise as described in greater detail herein. In response, theconsumer of cognitive insight data provides feedback to a learning 434agent, which is implemented as described in greater detail herein toprovide the feedback to the sourcing agent 318, at which point the CILSagents lifecycle 602 is continued. From the foregoing, skilledpractitioners of the art will recognize that each iteration of thecognitive agents lifecycle 602 provides more informed cognitiveinsights.

FIG. 7 is a simplified block diagram of a plurality of cognitiveplatforms implemented in accordance with an embodiment of the inventionwithin a hybrid cloud infrastructure. In this embodiment, the hybridcloud infrastructure 740 includes a cognitive cloud management 342component, a hosted 704 cognitive cloud environment, and a private 706network environment. As shown in FIG. 7 , the hosted 704 cognitive cloudenvironment includes a hosted 710 cognitive platform, such as thecognitive platform 310 shown in FIGS. 3, 4 a, and 4 b. In variousembodiments, the hosted 704 cognitive cloud environment may also includea hosted 718 universal knowledge repository and one or more repositoriesof curated public data 714 and licensed data 716. Likewise, the hosted710 cognitive platform may also include a hosted 712 analyticsinfrastructure, such as the cloud analytics infrastructure 344 shown inFIGS. 3 and 4 c.

As likewise shown in FIG. 7 , the private 706 network environmentincludes a private 720 cognitive platform, such as the cognitiveplatform 310 shown in FIGS. 3, 4 a, and 4 b. In various embodiments, theprivate 706 network cognitive cloud environment may also include aprivate 728 universal knowledge repository and one or more repositoriesof application data 724 and private data 726. Likewise, the private 720cognitive platform may also include a private 722 analyticsinfrastructure, such as the cloud analytics infrastructure 344 shown inFIGS. 3 and 4 c. In certain embodiments, the private 706 networkenvironment may have one or more private 736 cognitive applicationsimplemented to interact with the private 720 cognitive platform.

As used herein, a universal knowledge repository broadly refers to acollection of knowledge elements that can be used in various embodimentsto generate one or more cognitive insights described in greater detailherein. In various embodiments, these knowledge elements may includefacts (e.g., milk is a dairy product), information (e.g., an answer to aquestion), descriptions (e.g., the color of an automobile), skills(e.g., the ability to install plumbing fixtures), and other classes ofknowledge familiar to those of skill in the art. In these embodiments,the knowledge elements may be explicit or implicit. As an example, thefact that water freezes at zero degrees centigrade would be an explicitknowledge element, while the fact that an automobile mechanic knows howto repair an automobile would be an implicit knowledge element.

In certain embodiments, the knowledge elements within a universalknowledge repository may also include statements, assertions, beliefs,perceptions, preferences, sentiments, attitudes or opinions associatedwith a person or a group. As an example, user ‘A’ may prefer the pizzaserved by a first restaurant, while user ‘B’ may prefer the pizza servedby a second restaurant. Furthermore, both user ‘A’ and ‘B’ are firmly ofthe opinion that the first and second restaurants respectively serve thevery best pizza available. In this example, the respective preferencesand opinions of users ‘A’ and ‘B’ regarding the first and secondrestaurant may be included in the universal knowledge repository 880 asthey are not contradictory. Instead, they are simply knowledge elementsrespectively associated with the two users and can be used in variousembodiments for the generation of various cognitive insights, asdescribed in greater detail herein.

In various embodiments, individual knowledge elements respectivelyassociated with the hosted 718 and private 728 universal knowledgerepositories may be distributed. In one embodiment, the distributedknowledge elements may be stored in a plurality of data stores familiarto skilled practitioners of the art. In this embodiment, the distributedknowledge elements may be logically unified for various implementationsof the hosted 718 and private 728 universal knowledge repositories. Incertain embodiments, the hosted 718 and private 728 universal knowledgerepositories may be respectively implemented in the form of a hosted orprivate universal cognitive graph. In these embodiments, nodes withinthe hosted or private universal graph contain one or more knowledgeelements.

In various embodiments, a secure tunnel 730, such as a virtual privatenetwork (VPN) tunnel, is implemented to allow the hosted 710 cognitiveplatform and the private 720 cognitive platform to communicate with oneanother. In these various embodiments, the ability to communicate withone another allows the hosted 710 and private 720 cognitive platforms towork collaboratively when generating cognitive insights described ingreater detail herein. In various embodiments, the hosted 710 cognitiveplatform accesses knowledge elements stored in the hosted 718 universalknowledge repository and data stored in the repositories of curatedpublic data 714 and licensed data 716 to generate various cognitiveinsights. In certain embodiments, the resulting cognitive insights arethen provided to the private 720 cognitive platform, which in turnprovides them to the one or more private cognitive applications 736.

In various embodiments, the private 720 cognitive platform accessesknowledge elements stored in the private 728 universal knowledgerepository and data stored in the repositories of application data 724and private data 726 to generate various cognitive insights. In turn,the resulting cognitive insights are then provided to the one or moreprivate cognitive applications 736. In certain embodiments, the private720 cognitive platform accesses knowledge elements stored in the hosted718 and private 728 universal knowledge repositories and data stored inthe repositories of curated public data 714, licensed data 716,application data 724 and private data 726 to generate various cognitiveinsights. In these embodiments, the resulting cognitive insights are inturn provided to the one or more private cognitive applications 736.

In various embodiments, the secure tunnel 730 is implemented for thehosted 710 cognitive platform to provide 732 predetermined data andknowledge elements to the private 720 cognitive platform. In oneembodiment, the provision 732 of predetermined knowledge elements allowsthe hosted 718 universal knowledge repository to be replicated as theprivate 728 universal knowledge repository. In another embodiment, theprovision 732 of predetermined knowledge elements allows the hosted 718universal knowledge repository to provide updates 734 to the private 728universal knowledge repository. In certain embodiments, the updates 734to the private 728 universal knowledge repository do not overwrite otherdata. Instead, the updates 734 are simply added to the private 728universal knowledge repository.

In one embodiment, knowledge elements that are added to the private 728universal knowledge repository are not provided to the hosted 718universal knowledge repository. As an example, an airline may not wishto share private information related to its customer's flights, theprice paid for tickets, their awards program status, and so forth. Inanother embodiment, predetermined knowledge elements that are added tothe private 728 universal knowledge repository may be provided to thehosted 718 universal knowledge repository. As an example, the operatorof the private 720 cognitive platform may decide to licensepredetermined knowledge elements stored in the private 728 universalknowledge repository to the operator of the hosted 710 cognitiveplatform. To continue the example, certain knowledge elements stored inthe private 728 universal knowledge repository may be anonymized priorto being provided for inclusion in the hosted 718 universal knowledgerepository. In one embodiment, only private knowledge elements arestored in the private 728 universal knowledge repository. In thisembodiment, the private 720 cognitive platform may use knowledgeelements stored in both the hosted 718 and private 728 universalknowledge repositories to generate cognitive insights. Skilledpractitioners of the art will recognize that many such embodiments arepossible and the foregoing is not intended to limit the spirit, scope orintent of the invention.

FIG. 8 depicts a cognitive learning framework implemented in accordancewith an embodiment of the invention to perform cognitive learningoperations. As used herein, a cognitive learning operation broadlyrefers to the implementation of a cognitive learning technique,described in greater detail herein, to generate a cognitive learningresult. In various embodiments, the implementation of the learningtechnique is performed by a Cognitive Inference and Learning System(CILS), likewise described in greater detail herein.

In certain embodiments, the cognitive learning result is used by theCILS to update a knowledge model, described in greater detail herein. Invarious embodiments, the knowledge model is implemented as a universalknowledge repository, such as the hosted 718 and private 728 universalknowledge repositories depicted in FIG. 7 , or the universal knowledgerepositories 918 and 1080 respectively depicted in FIGS. 9 and 10 a. Incertain embodiments, the knowledge model is implemented as a cognitivegraph.

In various embodiments, the cognitive learning framework 800 may includevarious cognitive learning styles 802 and cognitive learning categories810. As used herein, a cognitive learning style broadly refers to ageneralized learning approach implemented by a CILS to perform acognitive learning operation. In various embodiments, the cognitivelearning styles 802 may include a declared 804 cognitive learning style,an observed 806 cognitive learning style, and an inferred 808 cognitivelearning style.

As used herein, a declared 804 cognitive learning style broadly refersto the use of declarative data by a CILS to perform a correspondingcognitive learning operation. In various embodiments, the declarativedata may be processed by the CILS as a statement, an assertion, or averifiable fact. For example, an electronic medical record (EMR) maycontain declarative data asserting that John Smith has Type 1 diabetes,which is a verifiable fact. As another example, a user may explicitlymake a declarative statement that they do not like sushi.

Likewise, as used herein, an observed 806 cognitive learning stylebroadly refers to the use of observed data by CILS to perform acorresponding cognitive learning operation. In various embodiments, theobserved data may include a pattern, a concept, or some combinationthereof. As an example, a CILS may receive and process a stream ofinformation, and over time, observe the formation of a discernablepattern, such as a user always ordering Chinese or Thai food fordelivery at lunchtime. In this example, the discerned pattern of theuser's ordering behavior may correspond to the concept that the user'slunchtime food preference is Asian cuisine.

In certain embodiments, a concept may include an observation of the useof certain words in a particular context. For example, the use of theword “haircut” in a financial text may refer to the difference betweenthe market value of an asset used as loan collateral and the amount ofthe loan, as opposed to a service performed by a hair stylist. In thisexample, natural language processing (NLP) approaches known to those ofskill in the art are implemented by the CILS during the performance ofcognitive learning operations to determine the context in which the word“haircut” is used.

As likewise used herein, an inferred 808 cognitive learning stylebroadly refers to the use of inferred data by a CILS to perform acorresponding cognitive learning operation. In various embodiments theinferred data may include data inferred from the processing of sourcedata. In certain embodiments, the inferred data may include conceptsthat are inferred from the processing of other concepts. In theseembodiments, the inferred data resulting from the processing of thesource data, the concepts, or a combination thereof, may result in theprovision of new information that was not in the source data or otherconcepts.

As an example, a user's selection of a particular accommodation in aresort area during a holiday may result in an inference they preferstaying at a bed and breakfast while on personal travel. Likewise, theselection of a four star accommodation in a downtown area on a weekdaymay result in an inference the same user prefers a luxury hotel while onbusiness travel. In this example, the user may not declaratively statean accommodation preference for a given type of travel. Likewise, theremay be insufficient data to observe a particular accommodationpreference, regardless of the type of travel.

In various embodiments, each of the cognitive learning styles 802 may beassociated with the use of a set of processing resources to perform acorresponding cognitive learning operation. As an example, the observed806 cognitive learning style may require more, or different, processingresources than the declared 804 cognitive learning style. Likewise, theinferred 808 cognitive learning style may require more, or different,processing resources than either the declared 804 or observed 806cognitive learning styles. The particular resources used by each ofcognitive learning styles 802 is a matter of design choice.

As used herein, a cognitive learning category 810 broadly refers to asource of information used by a CILS to perform cognitive learningoperations. In various embodiments, the cognitive learning categories810 may include a data-based 812 cognitive learning category and aninteraction-based 814 cognitive learning category. As used herein, adata-based 812 cognitive learning category broadly refers to the use ofdata as a source of information in the performance of a cognitivelearning operation by a CILS.

In various embodiments, the data may be provided to the CILS inreal-time, near real-time, or batch mode as it is performing cognitivelearning operations. In certain embodiments, the data may be provided tothe CILS as a result of a query generated by the CILS. In variousembodiments, the data is provided to the CILS by a cognitive agent,described in greater detail herein. In one embodiment, the cognitiveagent is a learning agent, likewise described in greater detail herein.

In certain embodiments, the data may be multi-structured data. In theseembodiments, the multi-structured data may include unstructured data(e.g., a document), semi-structured data (e.g., a social media post),and structured data (e.g., a string, an integer, etc.), such as datastored in a relational database management system (RDBMS). In variousembodiments, the data may be public, private, or a combination thereof.In certain embodiments the data may be provided by a device, stored in adata lake, a data warehouse, or some combination thereof.

As likewise used herein, an interaction-based 814 cognitive learningcategory broadly refers to the use of one or more results of aninteraction as a source of information used by a CILS to perform acognitive learning operation. In various embodiments, the interactionmay be between any combination of devices, applications, services,processes, or users. In certain embodiments, the results of theinteraction may be provided in the form of feedback data to the CILS.

In various embodiments, the interaction may be explicitly or implicitlyinitiated by the provision of input data to the devices, applications,services, processes or users. In certain embodiments, the input data maybe provided in response to a composite cognitive insight provided by aCILS. In one embodiment, the input data may include a user gesture, suchas a key stroke, mouse click, finger swipe, or eye movement. In anotherembodiment, the input data may include a voice command from a user. Inyet another embodiment, the input data may include data associated witha user, such as biometric data (e.g., retina scan, fingerprint, bodytemperature, pulse rate, etc.).

In yet still another embodiment, the input data may includeenvironmental data (e.g., current temperature, etc.), location data(e.g., geographical positioning system coordinates, etc.), device data(e.g., telemetry data, etc.), or other data provided by a device,application, service, process or user. Those of skill in the art willrealize that many such embodiments of cognitive learning styles 802 andcognitive learning categories 810 are possible, and the foregoing is notintended to limit the spirit, scope or intent of the invention.

As used herein, a cognitive learning technique refers to the use of acognitive learning style, in combination with a cognitive learningcategory, to perform a cognitive learning operation. In variousembodiments, individual cognitive learning techniques associated with aprimary cognitive learning style are respectively bounded by anassociated primary cognitive learning category. For example, as shown inFIG. 8 , the direct correlations 824 and explicit likes/dislikes 826cognitive learning techniques are both associated with the declared 804learning style and respectively bounded by the data-based 812 andinteraction-based 808 cognitive learning categories.

As likewise shown in FIG. 8 , the patterns and concepts 828 and behavior830 cognitive learning techniques are both associated with the observed806 cognitive learning style and likewise respectively bounded by thedata-based 812 and interaction-based 814 cognitive learning categories.Likewise, as shown in FIG. 8 , the concept entailment 832 and contextualrecommendation 834 cognitive learning techniques are both associatedwith the inferred 808 cognitive learning style and likewise respectivelybounded by the data-based 812 and interaction-based 814 cognitivelearning categories.

As used herein, a direct correlations 824 cognitive learning techniquebroadly refers to the implementation of a declared 804 cognitivelearning style, bounded by a data-based 812 cognitive learning category,to perform cognitive learning operations related to direct correlations.Examples of direct correlation include statistical relationshipsinvolving dependence, such as the correlation between the stature orother physical characteristics of parents and their biologicaloffspring. Another example of direct correlation would be thecorrelation between the resulting demand for a product offered at aprice in a corresponding geographic market.

As yet another example, a spreadsheet may contain three columns of data,none of which have an associated column header. The first and secondcolumns may contain names and the third column may contain dates. Inthis example, the first column may include names that are commonly usedas first names (e.g., Bob, Mary, etc.) and the second column may includenames that are commonly used as last names (e.g., Smith, Jones, etc.).As a result, there is a statistical likelihood that the third column maycontain birthdates that directly correlate to the first and last namesin the first and second columns.

As used herein, an explicit likes/dislikes 824 cognitive learningtechnique broadly refers to the implementation of a declared 812cognitive learning style, bounded by an interaction-based 806 cognitivelearning category, to perform cognitive learning operations related to auser's explicit likes/dislikes. In various embodiments, a user'sexplicit likes/dislikes may be declaratively indicated through thereceipt of user input data, described in greater detail herein.

For example, an online shopper may select a first pair of shoes that areavailable in a white, black and brown. The user then elects to view alarger photo of the first pair of shoes, first in white, then in black,but not brown. To continue the example, the user then selects a secondpair of shoes that are likewise available in white, black and brown. Asbefore, the user elects to view a larger photo of the second pair ofshoes, first in white, then in black, but once again, not brown. In thisexample, the user's online interaction indicates an explicit like forwhite and black shoes and an explicit dislike for brown shoes.

As used herein, a patterns and concepts 828 cognitive learning techniquebroadly refers to the implementation of an observed 812 cognitivelearning style, bounded by a data-based 804 cognitive learning category,to perform cognitive learning operations related to the observation ofpatterns and concepts. As an example, a database record may includeinformation related to various credit card transactions associated witha user. In this example, a pattern may be observed within the creditcard transactions that the user always uses rental cars when travelingbetween cities in California, but always uses trains when travelingbetween cities in New York, New Jersey, or Pennsylvania. By extension,this pattern may correspond to a concept that the user prefersautomobile transportation when traveling between cities on the Westcoast, but prefers train transportation when traveling between cities onthe East coast.

As another example, a CILS may receive and process a stream ofinformation, and over time, observe the formation of a discernablepattern, such as a user always selecting an Italian restaurant whensearching online for nearby places to eat. To continue the example, theCILS may observe that the user consistently orders a Neapolitan pizzafrom a particular Italian restaurant when location data received fromtheir mobile device indicates the user is in close proximity to therestaurant every Thursday. In this example, the discerned pattern of theuser's behavior in consistently ordering a Neapolitan pizza from aparticular restaurant when in close proximity on Thursdays maycorrespond to the concept that the user's food preference on Thursdaysis Italian cuisine.

As used herein, a behavior 830 cognitive learning technique broadlyrefers to the implementation of an observed 812 cognitive learningstyle, bounded by an interaction-based 808 cognitive learning category,to perform cognitive learning operations related to observed behaviors.In various embodiments, the observed behavior associated with aninteraction corresponds to various input data, likewise described ingreater detail herein. In certain embodiments, the observed behaviorsmay include observed behavior associated with interactions, described ingreater detail herein.

For example, a user may consistently place an online order for Mexican,Thai or Indian food to be delivered to their home in the evening. Tocontinue the example, promotional offers for fried chicken or seafoodare consistently ignored in the evening, yet consistently accepted atlunchtime. Furthermore, the observed behavior of the user is to acceptthe promotional offer that provides the most food at the lowest cost. Inthis example, the user's observed online behavior indicates a preferencefor spicy food in the evenings, regardless of price. Likewise, theuser's observed only behavior may indicate a preference for low cost,non-spicy foods for lunch.

As used herein, a concept entailment 832 cognitive learning techniquebroadly refers to the implementation of an inferred 808 cognitivelearning style, bounded by a data-based 804 cognitive learning category,to perform cognitive learning operations related to concept entailment.As likewise used herein, concept entailment broadly refers to theconcept of understanding language, within the context of one piece ofinformation being related to another. For example, if a statement ismade that implies ‘x’, and ‘x is known to imply ‘y’, then by extension,the statement may imply ‘y’ as well. In this example, there is achaining of evidence between the statement, ‘x’, and ‘y’ that may resultin a conclusion supported by the chain of evidence. As another example,based upon the study of philosophy, the statement that Socrates is aperson, and all people are mortal, then the implication is that Socratesis mortal.

As yet another example, psycho-social healthcare notes associated with aspecial needs child may include information resulting from a careprovider interviewing various family members. In this example, theconcept entailment 832 cognitive learning technique may be used by theCILS to process the notes. As a result, a set of risk factors, such astransportation challenges, education situations, the potential fordomestic abuse, and so forth, may be inferred that were not in theoriginal notes.

To continue the example, if the mother of a special needs child makes astatement that the family car is broken, then the statement implies thatthere may be a transportation issue. By extension, a transportationissue may imply that the mother may be unable to get the child to thehealthcare facility. Further, the inability of the child to get to thehealthcare facility may imply missing an appointment, which in turn mayimply that the child may not receive the care they have been prescribed.Taking the example one step further, if the child misses theirappointment, not only would they not receive their prescribed care, buthealthcare resources may not be used as optimally as possible.

As used herein, a contextual recommendation 834 cognitive learningtechnique broadly refers to the implementation of an inferred 808cognitive learning style, bounded by an interaction-based 814 cognitivelearning category, to perform cognitive learning operations related tocontextual recommendations provided to a user. As likewise used herein,a contextual recommendation broadly refers to a recommendation made to auser based upon a context.

As an example, a user may perform an online search for a casual,affordable restaurant that is nearby. To continue the example, the useris currently on a low-sodium, gluten-free diet that has been prescribedby their healthcare provider. Additionally, the healthcare provider hasrecommended that the user walk at least two miles every day. To furthercontinue the example, there may be five casual, affordable restaurantsthat are in close proximity to the location coordinates provided by theuser's mobile device, all of which are presented to the user forconsideration.

In response, the user further requests distance information to each ofthe restaurants, followed by a request to show only those restaurantsoffering low-sodium, gluten free menu items. As a result of the userinteraction, the CILS responds with directions to the only restaurantoffering low-sodium, gluten-free dishes. Further, the CILS may recommendthe user try a Mediterranean dish, as past interactions has indicatedthat the user enjoys Mediterranean cuisine. In this example, thecontextual recommendation is inferred from a series of interactions withthe user.

As a continuation of a prior example, a special needs child may have anappointment at a healthcare facility for a prescribed procedure.However, there is a transportation issue, due to the family automobilebeing broken. In this example, the inference is the child will misstheir appointment unless alternative transportation is arranged.Continuing the example, a contextual recommendation may be made to askthe healthcare facility to provide alternative transportation at theirexpense, which could then be interactively offered to the patient'smother, who in turn may accept the offer.

In various embodiments, machine learning algorithms 816 are respectivelyimplemented with a cognitive learning technique by a CILS whenperforming cognitive learning operations. In one embodiment, asupervised learning 818 machine learning algorithm may be implementedwith a direct correlations 824 cognitive learning technique, an explicitlikes/dislikes 826 cognitive learning technique, or both.

In another embodiment, an unsupervised learning 820 machine learningalgorithm may be implemented with a patterns and concepts 828 cognitivelearning technique, a behavior 830 cognitive learning technique, orboth. In yet another embodiment, a probabilistic reasoning 822 machinelearning algorithm may be implemented with a concept entailment 832cognitive learning technique, a contextual recommendation 834 cognitivelearning technique, or both. Skilled practitioners of the art willrecognize that many such embodiments are possible and the foregoing isnot intended to limit the spirit, scope or intent of the invention.

As used herein, a supervised learning 818 machine learning algorithmbroadly refers to a machine learning approach for inferring a functionfrom labeled training data. The training data typically consists of aset of training examples, with each example consisting of an inputobject (e.g., a vector) and a desired output value (e.g., a supervisorysignal). In various embodiments, a supervised learning algorithm isimplemented to analyze the training data and produce an inferredfunction, which can be used for mapping new examples.

As used herein, an unsupervised learning 820 machine learning algorithmbroadly refers to a machine learning approach for finding non-obvious orhidden structures within a set of unlabeled data. In variousembodiments, the unsupervised learning 820 machine learning algorithm isnot given a set of training examples. Instead, it attempts to summarizeand explain key features of the data it processes. Examples ofunsupervised learning approaches include clustering (e.g., k-means,mixture models, hierarchical clustering, etc.) and latent variablemodels (e.g., expectation-maximization algorithms, method of moments,blind signal separation techniques, etc.).

As used herein, a probabilistic reasoning 822 machine learning algorithmbroadly refers to a machine learning approach that combines the abilityof probability theory to handle uncertainty with the ability ofdeductive logic to exploit structure. More specifically, probabilisticreasoning attempts to find a natural extension of traditional logictruth tables. The results they define are derived through probabilisticexpressions instead.

In various embodiments, reinforcement learning 836 approaches areimplemented by a CILS in combination with a patterns and concepts 828, abehavior 830, a concept entailment 832, or a contextualizationrecommendation 834 cognitive learning technique when performingcognitive learning operations. As used herein, reinforcement learningbroadly refers to machine learning approaches inspired by behavioristpsychology, where software agents take actions within an environment tomaximize a notion of cumulative reward. Those of skill in the art willbe familiar with such reinforcement approaches, which are commonly usedin game theory, control theory, operations research, information theory,simulation-based optimization, multi-agent systems, swarm intelligence,statistics, and genetic algorithms.

In various embodiments, a particular cognitive learning technique mayinclude the implementation of certain aspects of a secondary cognitivelearning style, aspects of a secondary learning category, or acombination thereof. As an example, the patterns and concepts 828cognitive learning technique may include implementation of certainaspects of the direct correlations 824 and concept entailment 832cognitive learning techniques, and by extension, implementation ofcertain aspects of the declared 804 and inferred 808 cognitive learningstyles.

As another example, the explicit likes/dislikes 826 cognitive learningtechnique may include implementation of certain aspects of the directcorrelations 824 learning technique, and by extension, implementation ofcertain aspects of the declared 804 cognitive learning style. As yetanother example, the behavior 830 cognitive learning technique mayinclude certain aspects of both the patterns an concepts 828 andexplicit likes/dislikes 826 cognitive learning techniques, and byextension, implementation of certain aspects the data-based 812cognitive learning category. Skilled practitioners of art will recognizethat many such examples are possible and the foregoing is not intendedto limit the spirit, scope or intent of the invention.

In various embodiments, the data-based 812 cognitive learning category,machine learning algorithms 818, and the interaction-based 814 cognitivelearning category are respectively associated with the source 840,process 842 and deliver 844 steps of a cognitive learning process. Asused herein, a cognitive learning process broadly refers to a series ofcognitive learning steps performed by a CILS to generate a cognitivelearning result.

As likewise used herein, a source 840 step of a cognitive learningprocess broadly refers to operations associated with the acquisition ofdata used by a CILS to perform a cognitive learning operation. Likewise,as used herein, a process 842 step of a cognitive learning processbroadly refers to the use of individual machine learning algorithms 816by a CILS to perform cognitive learning operations. As likewise usedherein, a deliver 844 step of a cognitive learning process broadlyrefers to the delivery of a cognitive insight, which results in aninteraction, described in greater detail herein. Information related to,or resulting from, the interaction is then used by a CILS to performcognitive learning operations.

In various embodiments, the cognitive insight is delivered to a device,an application, a service, a process, a user, or a combination thereof.In certain embodiments, the resulting interaction information islikewise received by a CILS from a device, an application, a service, aprocess, a user, or a combination thereof. In various embodiments, theresulting interaction information is provided in the form of feedbackdata to the CILS. In these embodiments, the method by which thecognitive learning process, and its associated cognitive learning steps,is implemented is a matter of design choice. Skilled practitioners ofthe art will recognize that many such embodiments are possible and theforegoing is not intended to limit the spirit, scope or intent of theinvention.

FIG. 9 is a simplified block diagram of a Cognitive Learning andInference System (CILS) implemented in accordance with an embodiment ofthe invention to manage the performance of cognitive learning operationsthroughout their lifecycle. In various embodiments, individual elementsof a CILS are implemented within a massively parallel and portable cloudinsights fabric 902. In this embodiment, the individual elements of theCILS include repositories of multi-structured data 904, a universalknowledge repository 918, various shared analytics services 930, a deepcognition engine 944, and a cognitive insights as a service 946 module.

In various embodiments, the repositories of multi-structured data 904may include public 906, proprietary 908, social 910, device 912, andother types of data. Examples of such data include emails, social mediafeeds, news feeds, blogs, doctor's notes, transaction records, calllogs, and device telemetry streams. In these embodiments, therepositories of multi-structured data 904 may include unstructured data(e.g., a document), semi-structured data (e.g., a social media post),and structured data (e.g., a string, an integer, etc.), such as datastored in a relational database management system (RDBMS). In variousembodiments, such data may be stored in a data lake 914, a datawarehouse 916, or some combination thereof.

As shown in FIG. 9 , the universal knowledge repository 918 includesvarious cognitive agents 920, described in greater detail herein, datasubscription services 922, and a cognitive knowledge model 924. Incertain embodiments, the cognitive agents 920 include a learning agent.As likewise shown in FIG. 9 , the universal knowledge repository alsoincludes a fault-tolerant data compute architecture 926, familiar tothose of skill in the art, and a data sovereignty, security, lineage andtraceability system 928.

In various embodiments, individual data subscription services 922 areimplemented to deliver 956 data on an event-driven basis to the variousshared analytics services 930. In these embodiments, the data providedto the shared analytics services 930 is retrieved from the cognitiveknowledge model 924. In various embodiments, the cognitive knowledgemodel 924 is implemented as one or more cognitive graphs. In certainembodiments, the cognitive graph may be implemented as an applicationcognitive graph, a cognitive session graph, a cognitive persona, or acognitive profile, all of which are described in greater detail herein.The method by which the data is provided to the shared analyticsservices 930 by the individual data subscription services 922 is amatter of design choice.

In various embodiments, the fault-tolerant data compute architecture 926is implemented to provide an operational framework capable of reliablysupporting the other elements of the universal knowledge repository 918.In these embodiments, fault-tolerant approaches familiar to those ofskill in the art are implemented to accommodate needs to perform variouscognitive learning operations described in greater detail herein. Themethod by which these approaches are implemented is a matter of designchoice.

In various embodiments, the data sovereignty, security, lineage andtraceability system 928 is implemented to ensure that data ownershiprights are observed, data privacy is safeguarded, and data integrity isnot compromised. In certain embodiments, data sovereignty, security,lineage and traceability system 928 is likewise implemented to provide arecord of not only the source of the data throughout its lifecycle, butalso how it has been used, by whom, and for what purpose. Those of skillin the art will recognize many such embodiments are possible and theforegoing is not intended to limit the spirit, scope or intent of theinvention.

In this embodiment, the shared analytics services 930 includes NaturalLanguage Processing (NLP) 932 services, development services 934,models-as-a-service 936, management services 938, profile services 940,and ecosystem services 942. In various embodiments, the NLP 932 servicesinclude services related to the provision and management of NLPapproaches and processes known to skilled practitioners of the art. Inthese embodiments, NLP 932 services are implemented by a CILS during theperformance of cognitive learning operations, as described in greaterdetail herein. The method by which individual NLP 932 services areimplemented by the CILS is a matter of design choice.

In various embodiments, the development services 934 include servicesrelated to the management of data and models as they relate to thedevelopment of various analytic approaches known skilled practitionersof the art. In certain embodiments, the models-as-a-service 936 includesservices for the management and provision of a model. In variousembodiments, the models as a service 936 may be implemented to createand provide a model composed of other models. In this embodiment, themethod by which the models-as-a-service 936 is implemented to create andprovide such a composite model is a matter of design choice. In certainembodiments, the management services 938 include services related to themanagement and provision of individual services associated with, or apart of, the shared analytics services 930.

In various embodiments, the profile services 940 include servicesrelated to the provision and management of cognitive personas andcognitive profiles used by a CILS when performing a cognitive learningoperation. As used herein, a cognitive persona broadly refers to anarchetype user model that represents a common set of attributesassociated with a hypothesized group of users. In various embodiments,the common set of attributes may be described through the use ofdemographic, geographic, psychographic, behavioristic, and otherinformation. As an example, the demographic information may include agebrackets (e.g., 25 to 34 years old), gender, marital status (e.g.,single, married, divorced, etc.), family size, income brackets,occupational classifications, educational achievement, and so forth.Likewise, the geographic information may include the cognitive persona'stypical living and working locations (e.g., rural, semi-rural, suburban,urban, etc.) as well as characteristics associated with individuallocations (e.g., parochial, cosmopolitan, population density, etc.).

The psychographic information may likewise include information relatedto social class (e.g., upper, middle, lower, etc.), lifestyle (e.g.,active, healthy, sedentary, reclusive, etc.), interests (e.g., music,art, sports, etc.), and activities (e.g., hobbies, travel, going tomovies or the theatre, etc.). Other psychographic information may berelated to opinions, attitudes (e.g., conservative, liberal, etc.),preferences, motivations (e.g., living sustainably, exploring newlocations, etc.), and personality characteristics (e.g., extroverted,introverted, etc.) Likewise, the behavioristic information may includeinformation related to knowledge and attitude towards variousmanufacturers or organizations and the products or services they mayprovide.

In various embodiments, the behavioristic information is used by abehavior learning technique, described in greater detail herein, in theperformance of a cognitive learning operation. To continue the example,the behavioristic information may be related to brand loyalty, interestin purchasing a product or using a service, usage rates, perceivedbenefits, and so forth. Skilled practitioners of the art will recognizethat many such attributes are possible and the foregoing is not intendedto limit the spirit, scope or intent of the invention.

In various embodiments, one or more cognitive personas may be associatedwith a user. In certain embodiments, a cognitive persona is selected andthen used by a CILS to generate one or more composite cognitive insightsas described in greater detail herein. In these embodiments, thecomposite cognitive insights that are generated for a user as a resultof using a first cognitive persona may be different than the compositecognitive insights that are generated as a result of using a secondcognitive persona.

In various embodiments, provision of the composite cognitive insightsresults in the CILS receiving feedback 958 data from various individualusers and other sources, such as cognitive business processes andapplications 948. In one embodiment, the feedback 958 data is used torevise or modify the cognitive persona. In another embodiment, thefeedback 958 data is used to create a new cognitive persona. In yetanother embodiment, the feedback 958 data is used to create one or moreassociated cognitive personas, which inherit a common set of attributesfrom a source cognitive persona. In one embodiment, the feedback 958data is used to create a new cognitive persona that combines attributesfrom two or more source cognitive personas. In another embodiment, thefeedback 958 data is used to create a cognitive profile, described ingreater detail herein, based upon the cognitive persona. Those of skillin the art will realize that many such embodiments are possible and theforegoing is not intended to limit the spirit, scope or intent of theinvention.

As used herein, a cognitive profile refers to an instance of a cognitivepersona that references personal data associated with a user. In variousembodiments, the personal data may include the user's name, address,Social Security Number (SSN), age, gender, marital status, occupation,employer, income, education, skills, knowledge, interests, preferences,likes and dislikes, goals and plans, and so forth. In certainembodiments, the personal data may include data associated with theuser's interaction with a CILS and related composite cognitive insightsthat are generated and provided to the user. In various embodiments, theuser's interaction with a CILS may be provided to the CILS as feedback958 data.

In various embodiments, the personal data may be distributed. In certainof these embodiments, subsets of the distributed personal data may belogically aggregated to generate one or more cognitive profiles, each ofwhich is associated with the user. Skilled practitioners of the art willrecognize that many such embodiments are possible and the foregoing isnot intended to limit the spirit, scope or intent of the invention.

In various embodiments, a cognitive persona or cognitive profile isdefined by a first set of nodes in a weighted cognitive graph. In theseembodiments, the cognitive persona or cognitive profile is furtherdefined by a set of attributes that are respectively associated with aset of corresponding nodes in the weighted cognitive graph. In variousembodiments, an attribute weight is used to represent a relevance valuebetween two attributes. For example, a higher numeric value (e.g.,‘5.0’) associated with an attribute weight may indicate a higher degreeof relevance between two attributes, while a lower numeric value (e.g.,‘0.5’) may indicate a lower degree of relevance.

In various embodiments, the numeric value associated with attributeweights may change as a result of the performance of composite cognitiveinsight and feedback 958 operations described in greater detail herein.In one embodiment, the changed numeric values associated with theattribute weights may be used to modify an existing cognitive persona orcognitive profile. In another embodiment, the changed numeric valuesassociated with the attribute weights may be used to generate a newcognitive persona or cognitive profile. In certain embodiments, variousecosystem services 942 are implemented to manage various aspects of theCILS infrastructure, such as interaction with external services. Themethod by which these various aspects are managed is a matter of designchoice.

In various embodiments, the deep cognition engine 944 is implemented toprovide deep contextual understanding and interpretation as variouscognitive learning operations, described in greater detail herein, arebeing performed by a CILS. In certain embodiments, the deep cognitionengine 944 may include a perceive 506 phase, a relate 508 phase, anoperate 510 phase, a process and execute 512 phase, and a learn 514phase. In various embodiments, streams of data are sourced from therepositories of multi-structured data 904 are delivered 956 by sourcingagents, described in greater detail herein to the deep cognition engine944. In these embodiments, the source streams of data are dynamicallyingested in real-time during the perceive 506 phase, and based upon acontext, extraction, parsing, and tagging operations are performed onlanguage, text and images contained therein.

Automatic feature extraction and modeling operations are then performedwith the previously processed source streams of data during the relate508 phase to generate queries to identify related data. In variousembodiments, cognitive learning operations are performed during theoperate 510 phase to discover, summarize and prioritize variousconcepts, described in greater detail herein, which are in turn used togenerate actionable recommendations and notifications associated. Theresulting actionable recommendations and notifications are thenprocessed during the process and execute 512 phase to deliver 956composite cognitive insights, such as recommendations, to the cognitiveinsights as a service 946 module.

In various embodiments, features from newly-observed data areautomatically extracted from user interaction 950 during the learn 514phase to improve various analytical models. In these embodiments, thelearn 514 phase includes feedback 958 data associated with observationsgenerated during the relate 508 phase, which is provided to the perceive506 phase. Likewise, feedback 958 data on decisions resulting fromoperations performed during the operate 510 phase, and feedback 958 datarelated to results resulting from operations performed during theprocess and execute 512 phase, are also provided to the perceive 506phase.

In various embodiments, user interactions 950 result from operationsperformed during the process and execute 512 phase. In theseembodiments, data associated with the user interactions 950 is providedas feedback 958 data to the perceive 506 phase. As an example, a firstquery from a user may be submitted to the CILS system, which in turngenerates a first cognitive insight, which is then provided to the user.In response, the user may respond by providing a first response, orperhaps a second query, either of which is provided in the same contextas the first query. The CILS receives the first response or secondquery, performs various cognitive learning operations, and provides theuser a second cognitive insight. As before, the user may respond with asecond response or a third query, in the context of the first or secondquery. Once again, the CILS performs various cognitive learningoperations and provides the user a third cognitive insight, and soforth.

In various embodiments, data may be delivered 956 from the repositoriesof multi-structured data 904 to the universal knowledge repository 918,which in turn may deliver 956 data to individual shared analyticsservices 930. In turn, individual shared analytics services 930 maydeliver 956 resulting data to the deep cognition engine 944. Likewise,the deep cognition engine 944 may in turn deliver 956 data to thecognitive insights as a service 946. In turn, the cognitive insights asa service 946 module may deliver data to various cognitive businessprocesses and applications 948.

In certain embodiments, the data delivered 956 by the cognitive insightsas a service 946 to the various cognitive business processes andapplications 948 includes composite cognitive insights, described ingreater detail herein. In various embodiments, the various cognitivebusiness processes and applications 948 may provide data, includingcomposite cognitive insights, for interaction 950, described in greaterdetail herein. In certain embodiments, the interaction may include userinteraction resulting in the provision of user input data, likewisedescribed in greater detail herein.

In various embodiments, the interaction results in the provision offeedback 958 data to the various cognitive business processes andapplications 948, where it may be provided as feedback 958 data to thecognitive insights as a service 946 module. Likewise, the cognitiveinsights as a service 946 module may provide resulting feedback 958 datato the deep cognition engine 944 for processing. In turn, the deepcognition engine 944 may provide resulting feedback 958 data toindividual shared analytics services 930, which likewise may provideresulting feedback 958 data to the universal knowledge repository 918.

In certain embodiments, the feedback 958 data provided to the universalknowledge repository 918 is used, as described in greater detail herein,to update the cognitive knowledge model 924. In various embodiments, theuniversal knowledge repository 918 may likewise provide feedback 958data to various repositories of multi-structured data 904. In certainembodiments, the feedback 958 data is used to update repositories ofmulti-structured data 904. In these embodiments, the feedback 958 datamay include updated data, new data, metadata, or a combination thereof.

In various embodiments, a first CILS element may iteratively deliver 956data to, and receive resulting feedback 958 data from, a second CILSelement prior to the second CILS element delivers data to a third CILSelement. As an example, the universal knowledge repository 918 maydeliver 956 a first set of data to the NLP services 932, which resultsin a first set of feedback 958 data being returned to the universalknowledge repository 918. As a result of receiving the first set offeedback 958 data, the universal knowledge repository 918 may provide asecond set of data to the models-as-a-service 936, which results in thegeneration of a second set of data. In this example, the second set ofdata is then delivered 956 to the deep cognition engine 944.

In one embodiment, the feedback 958 data received as a result of aninteraction 950 is provided to each of the individual CILS elements. Inanother embodiment, feedback 958 data received from one CILS element ismodified before it is provided as modified feedback 958 data to anotherCILS element. In yet another embodiment, feedback 958 data received fromone CILS element is not modified before it is provided as unmodifiedfeedback 958 data to another CILS element. Skilled practitioners willrecognize that many such embodiments are possible and the foregoing isnot intended to limit the spirit, scope or intent of the invention.

In various embodiments, the CILS is implemented to manage the lifecycle960 of a cognitive learning operation. In this embodiment, the cognitivelearning operation lifecycle 960 includes a source 962, a learn 965, aninfer 966, an interpret 968 and an act 970 lifecycle phase. As shown inFIG. 9 , the source 962, a learn 965, an infer 966, an interpret 968 andact 970 lifecycle phases can interact with one another by providing andreceiving data between adjacent phases. In addition, the act 970 phasecan provide data to the source 962 phase. In certain embodiments, thedata the act 907 phase provides to the source 962 phase includedfeedback data resulting from an interaction, described in greater detailherein.

In various embodiments, the source 962 lifecycle phase is implemented toacquire data from the repositories of multi-structured data 904, whichin turn is provided to the universal knowledge repository 918. In oneembodiment, the data is provided to the cognitive knowledge model 924via the implementation of the fault-tolerant data compute architecture926. In another embodiment, the data sovereignty, security, lineage andtraceability system 928 is implemented to ensure that data ownershiprights are observed, data privacy is safeguarded, and data integrity isnot compromised during the source 962 lifecycle phase. In certainembodiments, data sovereignty, security, lineage and traceability system928 is likewise implemented to provide a record of not only the sourceof the data throughout its lifecycle, but also how it has been used, bywhom, and for what purpose.

In various embodiments, the learn 964 lifecycle phase is implemented tomanage cognitive learning operations being performed by a CILS, asdescribed in greater detail herein. In certain embodiments, cognitiveagents 920 are used in the performance of these cognitive learningoperations. In one embodiment, a learning agent is used in theperformance of certain cognitive learning operations, as described ingreater detail herein.

In various embodiments, the infer 966 lifecycle phase is implemented toperform cognitive learning operations, described in greater detailherein. In certain embodiments, an inferred learning style, described ingreater detail herein, is implemented by the CILS to perform thesecognitive learning operations. In one embodiment, a concept entailmentcognitive learning technique is implemented by the CILS to perform acognitive learning operation in the infer 966 lifecycle phase. Inanother embodiment, a contextual recommendation cognitive learningtechnique is implemented by the CILS to perform a cognitive learningoperation in the infer 966 lifecycle phase.

In these embodiments, the CILS may implement a probabilistic reasoningmachine learning algorithm, described in greater detail herein, incombination with the concept entailment or contextual recommendationcognitive learning technique. In certain embodiments, the CILS mayimplement a reinforcement learning approach, likewise described ingreater detail herein, in combination with the concept entailment orcontextual recommendation cognitive learning technique. Skilledpractitioners of the art will recognize that many such embodiments arepossible and the foregoing is not intended to limit the spirit, scope orintent of the invention.

In various embodiments, the interpret 968 lifecycle phase is implementedto interpret the results of a cognitive learning operation such thatthey are consumable by a recipient, and by extension, present it in aform that is actionable in the act 970 lifecycle phase. In variousembodiments, the act 970 lifecycle phase is implemented to support aninteraction 950, described in greater detail herein. In certainembodiments, the interaction 950 includes interactions with a user,likewise described in greater detail herein. Skilled practitioners ofthe art will recognize that many such embodiments are possible and theforegoing is not intended to limit the spirit, scope or intent of theinvention.

FIGS. 10 a and 10 b are a simplified process flow diagram of theperformance of cognitive learning operations by a Cognitive Inferenceand Learning System (CILS) implemented in accordance with an embodimentof the invention. As used herein, a cognitive learning operation broadlyrefers to the implementation of a cognitive learning technique,described in greater detail herein, to generate a cognitive learningresult. In various embodiments, the cognitive learning technique isimplemented in combination with the provision of a composite cognitiveinsight. As likewise used herein, a composite cognitive insight broadlyrefers to a set of cognitive insights generated as a result oforchestrating a set of independent cognitive agents, referred to hereinas insight agents.

In various embodiments, the insight agents use a cognitive graph, suchas an application cognitive graph 1082, as their data source torespectively generate individual cognitive insights. As used herein, anapplication cognitive graph 1082 broadly refers to a cognitive graphthat is associated with a business process or cognitive application 304.In certain embodiments, different cognitive business processes andapplications 304 may interact with different application cognitivegraphs 1082 to generate individual cognitive insights for a user. Invarious embodiments, the resulting individual cognitive insights arethen composed to generate a set of composite cognitive insights, whichin turn is provided to a user in the form of a cognitive insight summary1048.

In various embodiments, the orchestration of the selected insight agentsis performed by the cognitive insight/learning engine 330 shown in FIGS.3 and 4 a. In certain embodiments, a subset of insight agents isselected to provide composite cognitive insights to satisfy a graphquery 1044, a contextual situation, or some combination thereof. Invarious embodiments, input data related to the contextual situation isused by the CILS to perform a cognitive learning operation, as describedin greater detail herein. For example, it may be determined, as likewisedescribed in greater detail herein, that a particular subset of insightagents may be suited to provide a composite cognitive insight related toa particular user of a particular device, at a particular location, at aparticular time, for a particular purpose.

In certain embodiments, the insight agents are selected fororchestration as a result of receiving direct or indirect input from auser. In various embodiments, the direct user input may be a naturallanguage inquiry. In certain embodiments, the indirect user input mayinclude the location of a user's device or the purpose for which it isbeing used. As an example, the Geographical Positioning System (GPS)coordinates of the location of a user's mobile device may be received asindirect user input. As another example, a user may be using theintegrated camera of their mobile device to take a photograph of alocation, such as a restaurant, or an item, such as a food product. Incertain embodiments, the direct or indirect user input may includepersonal information that can be used to identify the user. Skilledpractitioners of the art will recognize that many such embodiments arepossible and the foregoing is not intended to limit the spirit, scope orintent of the invention.

In various embodiments, cognitive learning operations may be performedin various phases of a cognitive learning process. In this embodiment,these phases include a source 1034 phase, a learn 1036 phase, aninterpret/infer 1038 phase, and an act 1040 phase. In the source 1034phase, a instantiation of a cognitive platform 1010 sources social data1012, public data 1014, device data 1016, and proprietary data 1018 fromvarious sources as described in greater detail herein. In variousembodiments, an example of a cognitive platform 1010 instantiation isthe cognitive platform 310 shown in FIGS. 3, 4 a, and 4 b. In thisembodiment, the instantiation of a cognitive platform 1010 includes asource 1006 component, a process 1008 component, a deliver 1010component, a cleanse 1020 component, an enrich 1022 component, afilter/transform 1024 component, and a repair/reject 1026 component.Likewise, as shown in FIG. 10 a , the process 1008 component includes arepository of models 1028, described in greater detail herein.

In various embodiments, the process 1008 component is implemented toperform various composite insight generation and other processingoperations described in greater detail herein. In these embodiments, theprocess 1008 component is implemented to interact with the source 1006component, which in turn is implemented to perform various data sourcingoperations described in greater detail herein. In various embodiments,the sourcing operations are performed by one or more sourcing agents, aslikewise described in greater detail herein. The resulting sourced datais then provided to the process 1008 component. In turn, the process1008 component is implemented to interact with the cleanse 1020component, which is implemented to perform various data cleansingoperations familiar to those of skill in the art. As an example, thecleanse 1020 component may perform data normalization or pruningoperations, likewise known to skilled practitioners of the art. Incertain embodiments, the cleanse 1020 component may be implemented tointeract with the repair/reject 1026 component, which in turn isimplemented to perform various data repair or data rejection operationsknown to those of skill in the art.

Once data cleansing, repair and rejection operations are completed, theprocess 1008 component is implemented to interact with the enrich 1022component, which is implemented in various embodiments to performvarious data enrichment operations described in greater detail herein.Once data enrichment operations have been completed, the process 1008component is likewise implemented to interact with the filter/transform1024 component, which in turn is implemented to perform data filteringand transformation operations described in greater detail herein.

In various embodiments, the process 1008 component is implemented togenerate various models, described in greater detail herein, which arestored in the repository of models 1028. The process 1008 component islikewise implemented in various embodiments to use the sourced data togenerate one or more cognitive graphs, such as an application cognitivegraph 1082, as likewise described in greater detail herein. In variousembodiments, the process 1008 component is implemented to gain anunderstanding of the data sourced from the sources of social data 1012,public data 1014, device data 1016, and proprietary data 1018, whichassist in the automated generation of the application cognitive graph1082.

The process 1008 component is likewise implemented in variousembodiments to perform bridging 1046 operations, described in greaterdetail herein, to access the application cognitive graph 1082. Incertain embodiments, the bridging 1046 operations are performed bybridging agents, likewise described in greater detail herein. In variousembodiments, the application cognitive graph 1082 is accessed by theprocess 1008 component during the learn 1036 phase of the compositecognitive insight generation operations.

In various embodiments, a cognitive business process or application 304is implemented to receive input data associated with an individual useror a group of users. In these embodiments, the input data may be direct,such as a user query or mouse click, or indirect, such as the currenttime or Geographical Positioning System (GPS) data received from amobile device associated with a user. In various embodiments, theindirect input data may include contextual data, described in greaterdetail herein. Once it is received, the input data is then submitted1042 by the cognitive business process or application 304 to a graphquery engine 326 during the interpret/infer 1038 phase. In variousembodiments, an inferred learning style, described in greater detailherein, is implemented by the CILS to perform cognitive learningoperations. In certain embodiments, the CILS is likewise implemented tointerpret the results of the cognitive learning operations such thatthey are consumable by a recipient, and by extension, present it in aform that is actionable in the act 1040 phase. In various embodiments,the act 1040 phase is implemented to support an interaction 950,described in greater detail herein.

The submitted 1042 input data is then processed by the graph queryengine 326 to generate a graph query 1044, as described in greaterdetail herein. The graph query 1044 is then used to query theapplication cognitive graph 1082, which results in the generation of oneor more composite cognitive insights, likewise described in greaterdetail herein. In certain embodiments, the graph query 1044 usesknowledge elements stored in the universal knowledge repository 1080when querying the application cognitive graph 1082 to generate the oneor more composite cognitive insights.

In various embodiments, the graph query 1044 results in the selection ofa cognitive persona, described in greater detail herein, from arepository of cognitive personas ‘1’ through ‘n’ 1072, according to aset of contextual information associated with a user. In certainembodiments, the universal knowledge repository 1080 includes therepository of personas ‘1’ through ‘n’ 1072. In various embodiments,individual nodes within cognitive personas stored in the repository ofpersonas ‘1’ through ‘n’ 1072 are linked 1054 to corresponding nodes inthe universal knowledge repository 1080. In certain embodiments, nodeswithin the universal knowledge repository 1080 are likewise linked tonodes within the cognitive application graph 1082.

As used herein, contextual information broadly refers to informationassociated with a location, a point in time, a user role, an activity, acircumstance, an interest, a desire, a perception, an objective, or acombination thereof. In various embodiments, the contextual informationis used in combination with the selected cognitive persona to generateone or more composite cognitive insights for a user. In certainembodiments, the contextual information may likewise be used incombination with the selected cognitive persona to perform one or moreassociated cognitive learning operations. In one embodiment, thecomposite cognitive insights that are generated for a user as a resultof using a first set of contextual information in combination with theselected cognitive persona may be different than the composite cognitiveinsights that are generated as a result of using a second set ofcontextual information in combination with the same cognitive persona.

In another embodiment, the result of using a first set of contextualinformation in combination with the selected cognitive persona toperform an associated cognitive learning operation may be different thanthe result of using a second set of contextual information incombination with the selected cognitive persona to perform the samecognitive learning operation. In yet another embodiment, the compositecognitive insights that are generated for a user as a result of using aset of contextual information with a first cognitive persona may bedifferent than the composite cognitive insights that are generated as aresult of using the same set of contextual information with a secondcognitive persona. In yet still another embodiment, the result of usinga set of contextual information in combination with a first cognitivepersona to perform an associated cognitive learning operation may bedifferent than the result of using the same set of contextualinformation in combination with a second cognitive persona to performthe same cognitive learning operation.

As an example, a user may have two associated cognitive personas,“purchasing agent” and “retail shopper,” which are respectively selectedaccording to two sets of contextual information. In this example, the“purchasing agent” cognitive persona may be selected according to afirst set of contextual information associated with the user performingbusiness purchasing activities in their office during business hours,with the objective of finding the best price for a particular commercialinventory item. Conversely, the “retail shopper” cognitive persona maybe selected according to a second set of contextual informationassociated with the user performing cognitive personal shoppingactivities in their home over a weekend, with the objective of finding adecorative item that most closely matches their current furnishings.

Those of skill in the art will realize that the composite cognitiveinsights generated as a result of combining the first cognitive personawith the first set of contextual information will likely be differentthan the composite cognitive insights generated as a result of combiningthe second cognitive persona with the second set of contextualinformation. Likewise, the result of a cognitive learning operation thatuses the first cognitive persona in combination with the first set ofcontextual information will likely be different that the result of acognitive learning operation that uses a second cognitive persona incombination with a second set of contextual information.

In various embodiments, the graph query 1044 results in the selection ofa cognitive profile, described in greater detail herein, from arepository of cognitive profiles ‘1’ through ‘n’ 1074 according toidentification information associated with a user. The method by whichthe identification information is determined is a matter of designchoice. In certain embodiments, a set of contextual informationassociated with a user is used to select a cognitive profile from therepository of cognitive profiles ‘1’ through ‘n’ 1074. In variousembodiments, one or more cognitive profiles may be associated with auser.

In these embodiments, a cognitive profile is selected and then used by aCILS to generate one or more composite cognitive insights for the useras described in greater detail herein. In certain of these embodiments,the selected cognitive profile provides a basis for adaptive changes tothe CILS, and by extension, the composite cognitive insights itgenerates. In various embodiments, a cognitive profile may likewise byselected and then used by a CILS to perform one or more cognitivelearning operations as described in greater detail herein. In certain ofthese embodiments, the results of the one or more cognitive learningoperations may likewise provide a basis for adaptive changes to theCILS, and by extension, the use of cognitive learning techniques in theperformance of subsequent cognitive learning operations.

In various embodiments, provision of the composite cognitive insightsresults in the CILS receiving feedback 1062 information related to anindividual user. In one embodiment, the feedback 1062 information isused to revise or modify a cognitive persona. In another embodiment, thefeedback 1062 information is used to revise or modify the cognitiveprofile associated with a user. In yet another embodiment, the feedback1062 information is used to create a new cognitive profile, which inturn is stored in the repository of cognitive profiles ‘1’ through ‘n’1074. In still yet another embodiment, the feedback 1062 information isused to create one or more associated cognitive profiles, which inherita common set of attributes from a source cognitive profile. In anotherembodiment, the feedback 1062 information is used to create a newcognitive profile that combines attributes from two or more sourcecognitive profiles. In various embodiments, these persona and profilemanagement operations 1076 are performed through interactions betweenthe cognitive business processes and applications 304, the repository ofcognitive personas ‘1’ through ‘n’ 1072, the repository of cognitiveprofiles ‘1’ through ‘n’ 1074, the universal knowledge repository 1080,or some combination thereof.

In various embodiments, the feedback 1062 is generated as a result of aninteraction 950. In various embodiments, the interaction 950 may bebetween any combination of devices, applications, services, processes,or users. In certain embodiments, the interaction 950 may be explicitlyor implicitly initiated by the provision of input data to the devices,applications, services, processes or users. In various embodiments, theinput data may be provided in response to a composite cognitive insightprovided by a CILS. In one embodiment, the input data may include a usergesture, such as a key stroke, mouse click, finger swipe, or eyemovement. In another embodiment, the input data may include a voicecommand from a user.

In yet another embodiment, the input data may include data associatedwith a user, such as biometric data (e.g., retina scan, fingerprint,body temperature, pulse rate, etc.). In yet still another embodiment,the input data may include environmental data (e.g., currenttemperature, etc.), location data (e.g., geographical positioning systemcoordinates, etc.), device data (e.g., telemetry data, etc.), or otherdata provided by a device, application, service, process or user. Inthese embodiments, the feedback 1062 may be used to perform variouscognitive learning operations, the results of which are used to update acognitive persona or profile associated with a user. Those of skill inthe art will realize that many such embodiments are possible and theforegoing is not intended to limit the spirit, scope or intent of theinvention.

In various embodiments, a cognitive profile associated with a user maybe either static or dynamic. As used herein, a static cognitive profilerefers to a cognitive profile that contains identification informationassociated with a user that changes on an infrequent basis. As anexample, a user's name, Social Security Number (SSN), or passport numbermay not change, although their age, address or employer may change overtime. To continue the example, the user may likewise have a variety offinancial account identifiers and various travel awards programidentifiers which change infrequently.

As likewise used herein, a dynamic cognitive profile refers to acognitive profile that contains information associated with a user thatchanges on a dynamic basis. For example, a user's interests andactivities may evolve over time, which may be evidenced by associatedinteractions 950 with the CILS. In various embodiments, theseinteractions 950 result in the provision of associated compositecognitive insights to the user. In certain embodiments, theseinteractions 950 may likewise be used to perform one or more associatedcognitive learning operations, the results of which may in turn be usedto generate an associated composite cognitive insight. In theseembodiments, the user's interactions 950 with the CILS, and theresulting composite cognitive insights that are generated, are used toupdate the dynamic cognitive profile on an ongoing basis to provide anup-to-date representation of the user in the context of the cognitiveprofile used to generate the composite cognitive insights.

In various embodiments, a cognitive profile, whether static or dynamic,is selected according to a set of contextual information associated witha user. In certain embodiments, the contextual information is likewiseused in combination with the selected cognitive profile to generate oneor more composite cognitive insights for the user. In variousembodiments, the contextual information may likewise be used incombination with the selected cognitive profile perform one or moreassociated cognitive learning operations. In one embodiment, thecomposite cognitive insights that are generated as a result of using afirst set of contextual information in combination with the selectedcognitive profile may be different than the composite cognitive insightsthat are generated as a result of using a second set of contextualinformation with the same cognitive profile.

In another embodiment, the result of using a first set of contextualinformation in combination with the selected cognitive profile toperform an associated cognitive learning operation may be different thanthe result of using a second set of contextual information incombination with the selected cognitive profile to perform the samecognitive learning operation. In yet another embodiment, the compositecognitive insights that are generated for a user as a result of using aset of contextual information with a first cognitive profile may bedifferent than the composite cognitive insights that are generated as aresult of using the same set of contextual information with a secondcognitive profile. In yet still another embodiment, the result of usinga set of contextual information in combination with a first cognitiveprofile to perform an associated cognitive learning operation may bedifferent than the result of using the same set of contextualinformation in combination with a second cognitive profile to performthe same cognitive learning operation.

As an example, a user may have two associated cognitive profiles,“runner” and “foodie,” which are respectively selected according to twosets of contextual information. In this example, the “runner” cognitiveprofile may be selected according to a first set of contextualinformation associated with the user being out of town on businesstravel and wanting to find a convenient place to run close to where theyare staying. To continue this example, two composite cognitive insightsmay be generated and provided to the user in the form of a cognitiveinsight summary 1248. The first may be suggesting a running trail theuser has used before and liked, but needs directions to find again. Thesecond may be suggesting a new running trail that is equally convenient,but wasn't available the last time the user was in town.

Conversely, the “foodie” cognitive profile may be selected according toa second set of contextual information associated with the user being athome and expressing an interest in trying either a new restaurant or aninnovative cuisine. To further continue this example, the user's“foodie” cognitive profile may be processed by the CILS to determinewhich restaurants and cuisines the user has tried in the last eighteenmonths. As a result, two composite cognitive insights may be generatedand provided to the user in the form of a cognitive insight summary1048. The first may be a suggestion for a new restaurant that is servinga cuisine the user has enjoyed in the past. The second may be asuggestion for a restaurant familiar to the user that is promoting aseasonal menu featuring Asian fusion dishes, which the user has nottried before.

Those of skill in the art will realize that the composite cognitiveinsights generated as a result of combining the first cognitive profilewith the first set of contextual information will likely be differentthan the composite cognitive insights generated as a result of combiningthe second cognitive profile with the second set of contextualinformation. Likewise, the result of a cognitive learning operation thatuses the first cognitive profile in combination with the first set ofcontextual information will likely be different that the result of acognitive learning operation that uses a second cognitive profile incombination with a second set of contextual information.

In various embodiments, a user's cognitive profile, whether static ordynamic, may reference data that is proprietary to the user, anorganization, or a combination thereof. As used herein, proprietary databroadly refers to data that is owned, controlled, or a combinationthereof, by an individual user or an organization, which is deemedimportant enough that it gives competitive advantage to that individualor organization. In certain embodiments, the organization may be agovernmental, non-profit, academic or social entity, a manufacturer, awholesaler, a retailer, a service provider, an operator of a cognitiveinference and learning system (CILS), and others.

In various embodiments, an organization may or may not grant a user theright to obtain a copy of certain proprietary information referenced bytheir cognitive profile. In certain embodiments, a first organizationmay or may not grant a user the right to obtain a copy of certainproprietary information referenced by their cognitive profile andprovide it to a second organization. As an example, the user may not begranted the right to provide travel detail information (e.g., traveldates and destinations, etc.) associated with an awards program providedby a first travel services provider (e.g., an airline, a hotel chain, acruise ship line, etc.) to a second travel services provider. In variousembodiments, the user may or may not grant a first organization theright to provide a copy of certain proprietary information referenced bytheir cognitive profile to a second organization. Those of skill in theart will recognize that many such embodiments are possible and theforegoing is not intended to limit the spirit, scope or intent of theinvention.

In various embodiments, a set of contextually-related interactionsbetween a cognitive business process or application 304 and theapplication cognitive graph 1082 are represented as a corresponding setof nodes in a cognitive session graph, which is then stored in arepository of session graphs ‘1’ through ‘n’ 1052. As used herein, acognitive session graph broadly refers to a cognitive graph whose nodesare associated with a cognitive session. As used herein, a cognitivesession broadly refers to a user, group of users, theme, topic, issue,question, intent, goal, objective, task, assignment, process, situation,requirement, condition, responsibility, location, period of time, or anycombination thereof. In various embodiments, the results of a cognitivelearning operation, described in greater detail herein, may be stored ina session graph.

As an example, the application cognitive graph 1082 may be unaware of aparticular user's preferences, which are likely stored in acorresponding user profile. To further the example, a user may typicallychoose a particular brand or manufacturer when shopping for a given typeof product, such as cookware. A record of each query regarding thatbrand of cookware, or its selection, is iteratively stored in a sessiongraph that is associated with the user and stored in a repository ofsession graphs ‘1’ through ‘n’ 1052. As a result, the preference of thatbrand of cookware is ranked higher, and is presented in response tocontextually-related queries, even when the preferred brand of cookwareis not explicitly referenced by the user. To continue the example, theuser may make a number of queries over a period of days or weeks, yetthe queries are all associated with the same cognitive session graphthat is associated with the user and stored in a repository of sessiongraphs ‘1’ through ‘n’ 1052, regardless of when each query is made. Inthis example, the record of each query is used to perform an associatedcognitive learning operation, the results of which may be stored in anassociated session graph.

As another example, a user queries a cognitive application 304 duringbusiness hours to locate an upscale restaurant located close their placeof business. As a result, a first cognitive session graph stored in arepository of session graphs ‘1’ through ‘n’ 1052 is associated with theuser's query, which results in the provision of composite cognitiveinsights related to restaurants suitable for business meetings. Tocontinue the example, the same user queries the same cognitiveapplication 304 during the weekend to locate a casual restaurant locatedclose to their home. As a result, a second cognitive session graphstored in a repository of session graphs ‘1’ through ‘n’ 1052 isassociated with the user's query, which results in the provision ofcomposite cognitive insights related to restaurants suitable for familymeals. In these examples, the first and second cognitive session graphsare both associated with the same user, but for two different purposes,which results in the provision of two different sets of compositecognitive insights.

As yet another example, a group of customer support representatives istasked with resolving technical issues customers may have with aproduct. In this example, the product and the group of customer supportrepresentatives are collectively associated with a cognitive sessiongraph stored in a repository of session graphs ‘1’ through ‘n’ 1052. Tocontinue the example, individual customer support representatives maysubmit queries related to the product to a cognitive application 304,such as a knowledge base application. In response, a cognitive sessiongraph stored in a repository of session graphs ‘1’ through ‘n’ 1052 isused, along with the universal knowledge repository 1080 and applicationcognitive graph 1082, to generate individual or composite cognitiveinsights to resolve a technical issue for a customer. In this example,the cognitive application 304 may be queried by the individual customersupport representatives at different times during some time interval,yet the same cognitive session graph stored in a repository of sessiongraphs ‘1’ through ‘n’ 1052 is used to generate composite cognitiveinsights related to the product.

In various embodiments, each cognitive session graph associated with auser and stored in a repository of session graphs ‘1’ through ‘n’ 1052includes one or more direct or indirect user queries represented asnodes, and the time at which they were asked, which are in turn linked1054 to nodes that appear in the application cognitive graph 1082. Incertain embodiments, each individual session graph that is associatedwith the user and stored in a repository of session graphs ‘1’ through‘n’ 1052 introduces edges that are not already present in theapplication cognitive graph 1082. More specifically, each of the sessiongraphs that is associated with the user and stored in a repository ofsession graphs ‘1’ through ‘n’ 1052 establishes various relationshipsthat the application cognitive graph 1082 does not already have.

In various embodiments, individual cognitive profiles in the repositoryof profiles ‘1’ through ‘n’ 1074 are respectively stored as sessiongraphs in the repository of session graphs 1052. In these embodiments,nodes within each of the individual cognitive profiles are linked 1054to nodes within corresponding cognitive session graphs stored in therepository of cognitive session graphs ‘1’ through ‘n’ 1054. In certainembodiments, individual nodes within each of the cognitive profiles arelikewise linked 1054 to corresponding nodes within various cognitivepersonas stored in the repository of cognitive personas ‘1’ through ‘n’1072.

In various embodiments, individual graph queries 1044 associated with asession graph stored in a repository of session graphs ‘1’ through ‘n’1052 are likewise provided to insight agents to perform various kinds ofanalyses. In certain embodiments, each insight agent performs adifferent kind of analysis. In various embodiments, different insightagents may perform the same, or similar, analyses. In certainembodiments, different agents performing the same or similar analysesmay be competing between themselves.

For example, a user may be a realtor that has a young, uppermiddle-class, urban-oriented clientele that typically enjoys eating attrendy restaurants that are in walking distance of where they live. As aresult, the realtor may be interested in knowing about new or popularrestaurants that are in walking distance of their property listings thathave a young, middle-class clientele. In this example, the user'squeries may result the assignment of insight agents to perform analysisof various social media interactions to identify such restaurants thathave received favorable reviews. To continue the example, the resultingcomposite insights may be provided as a ranked list of candidaterestaurants that may be suitable venues for the realtor to meet hisclients.

In various embodiments, the process 1008 component is implemented toprovide these composite cognitive insights to the deliver 1010component, which in turn is implemented to deliver the compositecognitive insights in the form of a cognitive insight summary 1048 tothe cognitive business processes and applications 304. In theseembodiments, the cognitive platform 1010 is implemented to interact withan insight front-end 1056 component, which provides a composite insightand feedback interface with the cognitive business processes andapplications 304. In certain embodiments, the insight front-end 1056component includes an insight Application Program Interface (API) 1058and a feedback API 1060, described in greater detail herein. In theseembodiments, the insight API 1058 is implemented to convey the cognitiveinsight summary 1048 to the cognitive business processes andapplications 304. Likewise, the feedback API 1060 is used to conveyassociated direct or indirect user feedback 1062 to the cognitiveplatform 1010. In certain embodiments, the feedback API 1060 providesthe direct or indirect user feedback 1062 to the repository of models1028 described in greater detail herein.

To continue the preceding example, the user may have received a list ofcandidate restaurants that may be suitable venues for meeting hisclients. However, one of his clients has a pet that they like to takewith them wherever they go. As a result, the user provides feedback 1062that he is looking for a restaurant that is pet-friendly. The providedfeedback 1062 is in turn provided to the insight agents to identifycandidate restaurants that are also pet-friendly. In this example, thefeedback 1062 is stored in the appropriate session graph 1052 associatedwith the user and their original query.

In various embodiments, as described in the descriptive text associatedwith FIGS. 5, 8 and 9 , cognitive learning operations are iterativelyperformed during the learn 1036 phase to provide more accurate anduseful composite cognitive insights. In certain of these embodiments,feedback 1062 received from the user is stored in a session graph thatis associated with the user and stored in a repository of session graphs‘1’ through ‘n’ 1052, which is then used to provide more accuratecomposite cognitive insights in response to subsequentcontextually-relevant queries from the user. In various embodiments, thefeedback 1062 received from the user is used to perform cognitivelearning operations, the results of which are then stored in a sessiongraph that is associated with the user. In these embodiments, thesession graph associated with the user is stored in a repository ofsession graphs ‘1’ through ‘n’ 1052.

As an example, composite cognitive insights provided by a particularinsight agent related to a first subject may not be relevant orparticularly useful to a user of the cognitive business processes andapplications 304. As a result, the user provides feedback 1062 to thateffect, which in turn is stored in the appropriate session graph that isassociated with the user and stored in a repository of session graphs‘1’ through ‘n’ 1052. Accordingly, subsequent insights provided by theinsight agent related the first subject may be ranked lower, or notprovided, within a cognitive insight summary 1048 provided to the user.Conversely, the same insight agent may provide excellent insightsrelated to a second subject, resulting in positive feedback 1062 beingreceived from the user. The positive feedback 1062 is likewise stored inthe appropriate session graph that is associated with the user andstored in a repository of session graphs ‘1’ through ‘n’ 1052. As aresult, subsequent insights provided by the insight agent related to thesecond subject may be ranked higher within a cognitive insight summary1048 provided to the user.

In various embodiments, the composite insights provided in eachcognitive insight summary 1048 to the cognitive business processes andapplications 304, and corresponding feedback 1062 received from a userin return, is provided to an associated session graph 1052 in the formof one or more insight streams 1064. In these and other embodiments, theinsight streams 1064 may contain information related to the user of thecognitive business processes and applications 304, the time and date ofthe provided composite cognitive insights and related feedback 1062, thelocation of the user, and the device used by the user.

As an example, a query related to upcoming activities that is receivedat 10:00 AM on a Saturday morning from a user's home may returncomposite cognitive insights related to entertainment performancesscheduled for the weekend. Conversely, the same query received at thesame time on a Monday morning from a user's office may return compositecognitive insights related to business functions scheduled during thework week. In various embodiments, the information contained in theinsight streams 1064 is used to rank the composite cognitive insightsprovided in the cognitive insight summary 1048. In certain embodiments,the composite cognitive insights are continually re-ranked as additionalinsight streams 1064 are received. Skilled practitioners of the art willrecognize that many such embodiments are possible and the foregoing isnot intended to limit the spirit, scope or intent of the invention.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A cognitive information processing systemenvironment comprising: a plurality of data sources; a cognitiveinference and learning system coupled to receive data from the pluralityof data sources, the cognitive inference and learning system processingthe data from the plurality of data sources to perform a cognitivelearning operation, the cognitive learning operation applying acognitive learning technique to generate a cognitive learning result,the cognitive inference and learning system comprising a cognitiveplatform, the cognitive platform comprising: a cognitive graph, thecognitive graph being derived from the plurality of data sources, thecognitive graph enabling the cognitive inference and learning system togenerate the cognitive learning result, the cognitive graph comprising aplurality of nodes, some of the plurality of nodes being linked withother of the plurality of nodes; a cognitive engine, the cognitiveengine comprising a dataset engine, a graph query engine and aninsight/learning engine, the dataset engine being implemented toestablish and maintain a dynamic data ingestion and enrichment pipeline,the graph query engine being implemented to receive and process queriessuch that the queries are bridged into the cognitive graph, bridging thequeries into the cognitive graph comprising interpreting the querieswithin a predetermined user context and then mapping the queries topredetermined nodes of the plurality of nodes within the cognitivegraph, the insight/learning engine being implemented to generate acognitive insight from the cognitive graph, the dataset engine, thegraph query engine and the insight/learning engine operatingcollaboratively to generate the cognitive learning result; and, adestination, the destination being updated based upon the learningresult, the destination comprising a knowledge model, the knowledgemodel being implemented as the cognitive graph.
 2. The cognitiveinformation processing system environment of claim 1, wherein: theknowledge model is updated by the cognitive platform using the cognitivelearning result.
 3. The cognitive information processing systemenvironment of claim 1, wherein: the plurality of data sources compriserepositories of multi-structured data, the repositories ofmulti-structured data comprising at least one of public datarepositories, private data repositories, social data repositories anddevice data repositories.
 4. The cognitive information processing systemenvironment of claim 1, wherein: the multi-structured data comprises atleast one of unstructured data, semi-structured data and structureddata.
 5. The cognitive information processing system environment ofclaim 1, wherein: the cognitive inference and learning systemiteratively performs the cognitive learning operation to iterativelyimprove the cognitive learning result over time.
 6. The cognitiveinformation processing system environment of claim 1, wherein: thecognitive inference and learning system comprises a universal knowledgerepository; and, the destination comprises the universal knowledgerepository.
 7. The cognitive information processing system environmentof claim 6, wherein: the universal knowledge repository comprises atleast one of cognitive agents, a cognitive knowledge model, afault-tolerant data compute architecture, and a data sovereignty,security, lineage and traceability system.
 8. The cognitive informationprocessing system environment of claim 1, wherein: the cognitiveinference and learning system comprises a shared analytics servicescomponent.
 9. The cognitive information processing system environment ofclaim 8, wherein: the shared analytics services component comprises atleast one of a Natural Language processing (NLP) services component, adevelopment services component, a models as a service component, amanagement services component, a profile services component and anecosystem services component.
 10. The cognitive information processingsystem environment of claim 1, wherein: the cognitive inference andlearning system comprises deep cognition engine.